INFORMATICA
MEDICA SLOVONICA
Strokovni prispevki
Upravljanje z varnostjo informacij v zdravstvenih organizacijah
9
Označevanje in odkrivanje pomenskih razmerij v medicinskih besedilih
19 Zdrave povezave
26 Elektronski zdravstveni zapis in "online"
zdravstvene storitve v osnovnem zdravstvu
QC Molecular Imaging by Proton Magnetic Resonance Imaging (MRI) and MR Spectroscopic Imaging (MRSI) in Neurodegeneration
56 Sodium Weighted Clinical Brain Magnetic Resonance Imaging at 4.23 Tesla and Inversion Recovery Pulse Sequence
73 Primerjava 2-D in 3-D slik vidnega polja
v načinu "threshold" z diferenčno metodo
Bilten SDMI
79 Na začetku mandata
81 Zaključki strokovnega srečanja E-zdravje za
boljše zdravje v Sloveniji, Zreče, 17.-18.6.2005
Revija Slovenskega društva za medicinsko informatiko
Informatica Medica Slovenica
LETNIK 10, ŠTEVILKA 1
ISSN 1318-2129
ISSN 1318-2145 on line edition
http://lsd.uni-mb.si/ims
1
GLAVNI UREDNIK
VSEBINA
Janez Stare
SOUREDNIKA
Jure Dimec Blaž Zupan
TEHNIČNI UREDNIK
Peter Juvan
UREDNIŠKI ODBOR
Gregor Anderluh Dimitar Hristovski Emil Hudomalj Marjan Mihelin Mojca Paulin Borut Peterlin Uroš Petrovič Vesna Prijatelj Vladislav Rajkovič Gaj Vidmar
BIVŠA GLAVNA UREDNIKA
Martin Bigec Peter Kokol
O REVIJI
Informatica Medica Slovenica je interdisciplinarna strokovna revija, ki objavlja prispevke s področja medicinske informatike, informatike v zdravstvu in zdravstveni negi, ter bioinformatike. Revija objavlja strokovne prispevke, znanstvene razprave, poročila o aplikacijah ter uvajanju informatike na področjih medicine in zdravstva, pregledne članke in poročila. Se posebej so dobrodošli prispevki, ki obravnavajo nove in aktualne teme iz naštetih področij.
Informatica Medica Slovenica je strokovna revija Slovenskega društva za medicinsko informatiko. Revija je dostopna na naslovu http://lsd.uni-mb.si/ims. Avtorji člankov naj svoje prispevke v elektronski obliki pošiljajo glavnemu uredniku po elektronski pošti na naslov janez.stare@mf.uni-lj.si. Revijo prejemajo vsi člani društva. Informacije o članstvu v društvu oziroma o naročanju na revijo so dostopne na tajništvu društva (Drago Rudel, drago.rudel@mf.uni-lj.si).
Strokovni prispevki
1 Drago Rudel
Upravljanje z varnostjo informacij v zdravstvenih organizacijah
9 Špela Vintar
Označevanje in odkrivanje pomenskih razmerij v medicinskih besedilih
19 Vicki Powers
Zdrave povezave
26 Rade Iljaž
Elektronski zdravstveni zapis in "online" zdravstvene storitve v osnovnem zdravstvu
35 Rakesh Sharma
Molecular Imaging by Proton Magnetic Resonance Imaging (MRI) and MR Spectroscopic Imaging (MRSI) in Neurodegeneration
56 Rakesh Sharma
Sodium Weighted Clinical Brain Magnetic Resonance Imaging at 4.23 Tesla and Inversion Recovery Pulse Sequence
73 Andrej Ikica, Uroš Prelesnik, Branko Ikica
Primerjava 2-D in 3-D slik vidnega polja v načinu "threshold" z diferenčno metodo
Bilten SDMI
79 Ivan Eržen
Na začetku mandata
81 Tomaž Marčun, Gregor Cerkvenik, Leo Ciglenečki, Ivan Eržen, Brane Leskošek, Jožica Leskovšek, Dorjan Marušič, Drago Rudel, Smiljana Slavec, Jože Zrimšek
Zaključki strokovnega srečanja E-zdravje za boljše zdravje v Sloveniji, Zreče, 17.-18.6.2005
Informatica Medica Slovenica 2004; 9(1-2)
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Strokovno-znanstveni prispevek
Upravljanje z varnostjo informacij v zdravstvenih organizacijah
Drago Rudel
Izvleček. Po mnenju vzdrževalcev IS v slovenskih zdravstvenih organizacijah je stanje varovanja informacij neustrezno glede na pomembnost informacij, ki se hranijo in izmenjujejo. Z načrtovano uvedbo elektronskega zdravstvenega zapisa (elektronska zdravstvena kartoteka pacienta) in izmenjavo elektronskih podatkov med zdravstvenimi organizacijami bodo potrebe po zagotavljanju varnosti še večje. Avtor predlaga, da zdravstvene organizacije oblikujejo poenoteno celostno varnostno politiko po priporočilih standarda ISO17799 (BS7799:2002). Slovensko zdravstvo potrebuje ustrezen nadzorni organ, ki bo izdelal za zdravstvo specifična merila in preverjal skladnost organizacij z zahtevami standarda. Dolgoročno naj bi bilo zagotavljanje skladnosti pogoj za vključitev zdravstvene organizacije v načrtovani nacionalni sistem elektronske izmenjave podatkov.
Information security management in healthcare organizations in Slovenia
Institucija avtorja: Medicinska fakulteta - Inštitut za biomedicinsko informatiko, Ljubljana; MKS Elektronski sistemi d.o.o., Ljubljana.
Kontaktna oseba: Drago Rudel, MKS d.o.o., Rožna dol. C.XVII/22b, 1000 Ljubljana. email: drago.rudel@mf.uni-lj.si.
Abstract. According to opinion of IT managers in Slovene healthcare institutions security of medical data and information is insufficient when related to their importance. In the future an exchange of electronic healthcare records is planned what will further increase information security requirements. The author suggest that all healthcare organizations in Slovenia should adopt a unified and global security policy based on recommendation of the ISO17799 (BS7799:2002) standard. Accordingly, Slovenia would need a credible monitoring institution to prepare healthcare specific criteria and assess compliance of an institution with the standard requirements. Achieving the compliance should be an entry ticket for a planned national healthcare electronic data exchange system.
■ Infor Med Slov: 2005; 10(1): 1-8
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Rudel D: Upravljanje z varnostjo informacij v zdravstvenih organizacijah
Uvod
Zagotavljanje varnosti informacij postaja vse bolj pereč problem vseh modernih informacijskih sistemov (IS), torej tudi IS v zdravstvu. Grožnja varnosti IS v zdravstvu pomeni grožnjo poslanstvu, poslovnim ciljem in poslovnemu uspehu, delovanju zdravstvene institucije in posredno zdravju pacientov.
V zdravstvu nastaja velika količina zaupnih osebnih podatkov, ki so vezani na telesno, duševno in mentalno zdravje ljudi. Hranijo se na različnih nosilcih: na papirju, slikah, filmih, magnetnih trakovih, gibkih diskih, zgoščenkah, v glavah zaposlenih... Med uporabniki se prenašajo bodisi fizično, elektronsko in tudi kot govorjena beseda. V drugih poslovnih IS se potrebna stopnja varovanja podatkov pogosto ocenjuje z vrednostjo podatkov. Na nekaterih področjih lahko to vrednost ocenimo kot npr. izguba zaradi začasnega izpada delovanja IS podjetja, cena ponovne vzpostavitve baze podatkov po izgubi itd. V zdravstvenih organizacijah (ZO) je vrednost podatkov težko oceniti, saj imajo poleg objektivne tudi veliko subjektivno vrednost. Razumljivo je torej, da so v ZO zahteve glede varnosti pri beleženju, hranjenju in prenosu teh podatkov raznolike in velike.
Varovanje informacij v zdravstvu ni nov proces. Zdravstvene delavce veže k varovanju že etični kodeks1, zakoni s področja zdravstvenega varstva2 in zdravstvene dejavnosti3, zakon o varstvu osebnih podatkov, zakon o zbirkah podatkov4 ter različni predpisi. Za varnost elektronskih podatkov in informacij uvajajo ZO številne tehnološke rešitve kot npr. varovan dostopov do IS z gesli ter požarnimi zidovi, protivirusna zaščita, kodiranje podatkov ob prenosu...
Načrtovani razvoj informatike v zdravstvu v Sloveniji kaže, da se bodo zahteve glede varovanja informacij še povečale. Načrtujemo uvedbo elektronskega zdravstvenega zapisa5 o pacientu. Vseboval bo večino podatkov, ki so se do sedaj nahajali v papirnatih kartotekah. Hkrati načrtujemo6 izmenjevanje teh podatkov med ZO,
kar pomeni, da bomo namesto ločenih IS ZO imeli povezan IS. Tak sistem bo bolj ranljiv kot posamezni IS, varnostna tveganja pa se bodo s tem še povečala. To bo povečalo zahteve za varno zajemanje, hranjenje in posredovanje podatkov med zdravstvenimi organizacijami. Sistem ne bo smel "puščati" nikjer, niti v tisti ZO, ki bodo imele najslabše razmere za varno delo s podatki oz. prenos podatkov.
Veliko skrbnikov IS v ZO navaja, da zgolj tehnološki ukrepi za zagotavljanje varnosti informacij niso zadostni in da je potreben celosten pristop k reševanju te problematike. Vsakodnevno delo jim potrjuje spoznanje, da menedžment ZO ne kaže pravega zanimanja za reševanje težav in prelaga reševanje problemov varovanja informacij nanje. Skrb za varnost je bolj ali manj serija "gasilskih" akcij, v katerih se poskrbi zgolj za tisto, kar že "gori".
Za izboljšanje varnostnih razmer v ZO pogosto ni pravega razumevanja in volje vodstva ZO. Pripravljenost za zagotavljanje večje stopnje varovanja informacij ne odseva dejstva, da so informacije v zdravstvu sploh pomembne. Vodstveni delavci se pogosto izgovarjajo, da za to ni zadostnih finančnih sredstev, čeprav je zagotavljanje varnosti povezano predvsem z organizacijo dela in ne s tehnologijo. V ZO prednostno namenjajo investicijska sredstva neposrednemu izvajanju zdravstvenih storitev, tehnične rešitve v informatiki pa običajno čakajo na ostanek investicijskih sredstev. Kaj rado pa se zgodi, da se najdejo investicijska sredstva, za katera pa se ne ve, kje bi bila najučinkovitejše vložena za povečanje varnosti, saj ZO običajno ne izdelajo ustreznih analiz in načrtov. Brez politike varovanja informacij ostajajo skrbniki IS v ZO nemočni, da bi celostno uredili varnostne razmere.
Velika stopnja sedanje in pričakovane ogroženosti informacij v zdravstvu kliče po ustreznem sistemskem pristopu, ki bi slonel na poenoteni politiki zagotavljanja ustrezne (dogovorjene) stopnje varnosti v vsaki ZO.
Informatica Medica Slovenica 2004; 9(1-2)
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Standard ISO17799 za upravljanje z varnostjo v zdravstvenih organizacijah
V zadnjem času je tudi v Sloveniji dozorelo spoznanje, da lahko z upoštevanjem splošno veljavnih (mednarodnih) standardov najučinkoviteje uredimo določeno področje dejavnosti. Spoznanje sloni na pozitivnih izkušnjah organizacij, ki so uvedle bodisi standard za upravljanje s kakovostjo (ISO 9001), za upravljanje z okoljem (ISO 14000), ali za upravljanje z zdravjem zaposlenih (ISO 18000). Skupni imenovalec teh zvenečih standardov je "upravljanje". Upravljati z varnostjo informacij pomeni zagotoviti zaupnost, celovitost in dostopnost informacij. Ali lahko torej pričakujemo, da bomo tudi na področju zagotavljanja varnosti informacij v zdravstvu najhitreje in najučinkoviteje prišli do poenotene politike varovanja informacij s pomočjo mednarodnih standardov? Bomo sprejeli evropsko rešitev, to je standard za upravljanje z varnostjo informacij ISO17799 (BS7799)?
Standard ima dva dela: ISO/IEC 17799:2003 in BS7799-2:2003. Prvi del ISO/IEC17797
imenujemo tudi "kodeks varovanja informacij" in podaja primer dobre prakse. Temelji na spoznanju, da je potrebno varnost upravljati tako kot kakovost izdelkov in storitev. Določa enoten pristop k razvijanju, uporabi in nadzoru varnostnih meril, načel in postopkov. Osnovna poglavja prvega dela standarda so:
1.	Politika varovanja informacij
2.	Organiziranost delovanja
3.	Razvrstitev in nadzor sredstev
4.	Varovanje v zvezi z osebjem
5.	Fizično in okoljsko varovanje
6.	Upravljanje s komunikacijami in obratovanjem
7.	Obvladovanje dostopa
8.	Razvijanje in vzdrževanje sistema
9.	Zagotavljanje neprekinjenega poslovanja
10.	Usklajenost z veljavno zakonodajo
Drugi del standarda BS7799-2:20038 je specifikacija z napotki za uporabo ter zbirka lastnosti, ki jim IS organizacije mora zadostiti, če se organizacija želi certificirati po standardu. V nadaljevanju bomo, kjer ločitev ni potrebna, za oba dela uporabljali oznako "ISO17799".
Zanimanje za certificiranje po standardu BS7799-2 po svetu hitro raste. Sredi leta 2005 je bilo certificiranih okoli 1.800 podjetij oz. ustanov, od tega več kot polovico na Japonskem (967), v Evropi okoli 20% (360), največ v Veliki Britaniji (210) in le eno slovensko podjetje.9
V nasprotju z obstoječo prakso ZO v Sloveniji standard ISO17799 poudarja, da so za varnost informacij v vsaki organizaciji odgovorni vsi zaposleni in ne le skrbniki IS. Prav tako poudarja osebno zavezanost vodstva organizacije za izdelavo in uresničevanje vseh faz varnostne politike.
Standard nadalje zahteva, da je varnostna politika usklajena s poslovnimi cilji ZO. Zagotavljanje varnosti po standardu zato ni zgolj strošek, temveč pripomoček za doseganje ciljev ZO. Skozi to prizmo pomeni ogrožanje varnosti informacij tudi grožnjo doseganju poslovnih ciljev. Kot kažejo primeri iz drugih poslovnih sistemov (npr. izgubljeni potni listi pri pošiljanju prek DHL kurirske službe pomladi l.2003 v Sloveniji), lahko izguba, odtujitev ali zloraba podatkov/informacij hitro zmanjša ugled organizacije, ki upravlja s podatki.
Zagotavljanje varnosti po standardu ISO17799 pomeni tudi, da je varnost potrebno vrednotiti tudi s stališča zagotavljanja neprekinjenega delovnega procesa. Varnostni incidenti lahko upočasnijo, ali pa v celoti prekinejo delovni proces. Vrednost informacij je zato po standardu
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Rudel D: Upravljanje z varnostjo informacij v zdravstvenih organizacijah
ISO 17799 potrebno oceniti tudi v luči škode, ki bi jo povzročila uresničitev katere od groženj IS.
Varnost je torej potrebno upravljati kot druge poslovne procese. Zagotavljanje varnosti skladno s standardom ISO17799 je tako v organizacijah postalo upravljavski in ne zgolj tehnološki proces.
Z vsemi opisanimi lastnostmi bi bil standard ustrezna osnova tudi za zagotavljanje dogovorjene politike varovanja informacij v ZO v Sloveniji.
Standard varovanja informacij v Sloveniji
V Sloveniji smo standard za varovanje informacij že nekoliko okusili. Leta 1998 je takratni Urad za standardizacijo pri Ministrstvu za znanost in tehnologijo RS izdal osnutek standarda PSIST BS7799:1995,10 ki pa ni dosegel statusa SIST standarda (PSIST = slovenski standard v pripravi, SIST = Slovenski standard). To je bil prevod britanskega standarda BS7799-1995, ki pa so ga Britanci medtem že preklicali. Standard pa je bil leta 2003 proglašen tudi za slovenski standard v dveh delih:
•	SIST ISO/IEC 17799:2003 -
Informacijska tehnologija — Kodeks upravljanja varovanja informacij
•	SIST BS 7799-2:2003 - Sistemi za upravljanje varovanja informacij — Specifikacija z napotki za uporabo
Za uveljavljanje BS7799 standarda v Sloveniji je zelo pomembno dejstvo, da je Banka Slovenije sprejela PSIST BS-7799-1995 kot merilo zagotavljanja varnosti informacij v slovenskem bančništvu. Tako komercialni banki ne izda dovoljenja za delo, če njena politika varovanja podatkov ni usklajena standardom v pripravi
PSIST BS7799.11
področjih. Tudi v državni upravi RS dobiva standard ISO17799 svoje mesto.12 Nekdanji Center vlade za informatiko (CVI) je pripravil priporočila za pripravo informacijske varnostne politike,13 ki so zasnovana na standardu ISO17799. Vlada RS je kot lastnik, upravljavec in uporabnik informacijskih sistemov v letu 2002 zadolžila vsa ministrstva in njihove organe ter upravne enote, da pripravijo svoje politike varovanja informacij ter pričnejo izvajati vse ukrepe v zvezi z varovanjem na podlagi priporočil CVI izdala vsem državnim organom navodilo, da do uvajajo upravljavski sistem za varovanje informacij, ki ga definira standard BS7799.
Kot je bilo že omenjeno, imamo zaenkrat v Sloveniji le eno certificirano podjetje po standardu BS7799.9 Prav gotovo lahko v prihodnje pričakujemo povečano zanimanje za standard, saj bodo slovenska podjetja, ki sodelujejo s tujimi poslovnimi partnerji, prisiljena zagotoviti skladnosti s standardom, če bodo želela (še naprej) trgovati s tujimi poslovnimi partnerji.14
Povečane aktivnosti je mogoče zaslediti tudi pri institucijah oz. podjetjih, ki so povezane s certificiranjem. Slovenski inštitut za kakovost in meroslovje SIQ se pripravlja, da bo prevzel certificiranje organizacij za standard ISO17799, tako da bo njihov certifikat tudi mednarodno priznan. Slovenska svetovalna podjetja kot npr. Palsit,15 Danesa,16 Housing,17 Kivi se pripravljajo na pomoč ustanovam v Sloveniji pri zagotavljanju varnosti po priporočilih standarda oz. certificiranju po tem standardu, zato je že mogoče rekrutirati neodvisne svetovalce za področje informacijske varnosti.
Tudi pri zagotavljanju varnosti informacij v zdravstvu ob pomoči standarda ISO17799 v Sloveniji ne bomo orali ledine, niti ne bomo med zadnjimi v Evropi, ki bodo pristopili k sistemskemu reševanju problema s pomočjo omenjenega standarda.
Aktivnosti na področju varovanja informacij v skladu s standardom ISO17799 tečejo še na drugih
Informatica Medica Slovenica 2005; 10(1)
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Standard varovanja informacij v zdravstvu v tujini
Naštejmo le nekaj primerov prizadevanj za uvedbo tega standarda v zdravstvu:
Svet Evropske unije je skupaj z Evropsko komisijo izdelal strategijo na področju informacijske varnosti, ki med drugim vključuje ISO 17799 v upravljanje varnosti informacij v privatnih in javnih organizacijah.12
Pri Evropskem komiteju za standardizacijo CEN delujeta v okviru tehničnega komiteja CEN/TC251 "Zdravstvena informatika" dve delovni skupini (WG3 in WG4), ki se med drugim ukvarjata tudi z aktivnostmi, da se ISO17799 priporoči za uporabo v zdravstveni informatiki.18 Pri tem so aktivni tako Velika Britanija, Nizozemska, Avstrija, Nemčija in Švedska kot tudi Kanada, Japonska. Dejstvo je, da v posameznih državah ni posebnih certifikacijskih organov za področje zdravstva.
Delovna skupina pri "IMIA — International Medical Informatics Society" "WG4/Health -Informatics/Security" je leta 2001 pripravila predlog standarda za sistem javnih ključev v zdravstvu (ISO/DTS 17090). V predlogu so predpostavili, da bodo vsi uporabniki tega sistema v zdravstvu predhodno poskrbeli za varnost IS po standardu ISO17799.
V Veliki Britaniji je krovna zdravstvena organizacija NHS-National Health Service na zahtevo vlade opustila svoj prvotni sistem zagotavljanja varnosti informacij in vsem zdravstvenim organizacijam naložila, da za varnost informacij poskrbijo v skladu s standardom ISO/IEC17799:2000. Cilj NHS je, da se v nekaj letih vse zdravstvene organizacije v sistemu NHS (tudi splošni zdravniki), ki želijo imeti dostop do podatkov o pacientih v elektronski obliki (elektronski zdravstveni zapis) certificirajo po standardu BS7799-2. Trenutno izvajajo dvofazne pilotne projekte uvajanja ISO/IEC17799:2000v več pokrajinah Velike Britanije.19 NHS organizira
in sponzorira izvajanje seminarjev po UK za top NHS menedžment, da zagotovi podporo uvedbi standarda. Vzporedno izvajajo informacijsko-edukacijsko dejavnost ter aktivnosti za pridobitev mnenja ZO, na državnem nivoju pa ustanavljajo tudi nadzorno institucijo. Vsaka organizacija mora imeti tudi nadzorni organ za varovanje informacij o pacientu t.i. "Caldicott Guardian", ki ga običajno predstavlja starejši zdravstveni delavec. Prehod bo skupen za vse NHS organizacije zato, da bo pristop do varovanja informacij cenejši in učinkovitejši.
Certificiranje ali zagotavljanje skladnosti z ISO17799 v zdravstvene organizacije v Sloveniji?
Zagotavljanje skladnosti s standardom ISO17799 v ZO je potrebno gledati v luči urejanja razmer na področju upravljanja zdravstvenih IS v Sloveniji. Perspektivo je nakazal vladni projekt "Projekt razvoja upravljanja sistema zdravstvenega varstva -PRUSZV",6 ki je predvidel ustanovitev Centra za informatiko v zdravstvu, ki bi med drugim tudi pripravljal ustrezne politike in standarde za zdravstveno informatiko.
Prvo vprašanje glede zagotavljanja varnosti informacij v povezanem slovenskem zdravstvenem IS je, ali je potrebno, da bi se ZO certificirale po standardu oz. ali bi bilo dovolj, da bi zagotovile skladnost in morda kako drugače, brez certificiranja, potrdile zagotavljanje varnosti informacij skladno s standardom? Certificiranje ZO v Sloveniji ob podpori domačih strokovnjakov bi teoretično bilo mogoče, vendar je vprašljiv njegov smisel. Postopek je zahteven, dolgotrajen ter drag. Visoki so tako stroški pridobitve certifikata kot tudi stroški vzdrževanja njegove veljavnosti.
Verjetno bo za slovenske ZO bolj smotrno, če bi uporabile standard ISO17799 zgolj kot referenčno nivo, ki ga morajo v bližnji prihodnosti doseči vse
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Rudel D: Upravljanje z varnostjo informacij v zdravstvenih organizacijah
ZO. Izpolnjevanje priporočil standarda in uporaba ustreznih mehanizmov, ki jih predvideva standard, bi pomenila zagotavljanje zadostne mere varnosti informacij v ZO. Vsi stroški, ki bi pri tem nastajali, bi bili vezani neposredno na urejanje razmer v ZO in ustvarjanje takšnih pogojev dela v ustanovi, ki bi zagotavljali želeno (in za ZO priporočeno) stopnjo varnosti informacij. Zgolj zagotavljanje skladnosti s standardom ne zahteva plačevanje pristojbin certifikatskim institucijam.
Zaradi verodostojnosti "lastno ugotovljene skladnosti" pa bi bilo potrebno, da bi neodvisni organ s področja informatike v zdravstvu potrdil izpolnjevanje priporočil standarda v ZO. Takšen organ, ki ga sedaj nimamo, se bo moral formirati tudi zaradi drugih potreb v informatike v zdravstvu, bi lahko bil del načrtovanega Centra za informatiko v zdravstvu. Zaradi specifičnosti zdravstvenega področja bi moral ta organ pripraviti merila ocenjevanja, vrednotenja, nadzora itd. za varnost informacij. Standard ISO17799 sam namreč ne daje konkretnih meril za vrednotenje v zdravstvu, zato bo potrebno merila doreči na nacionalnem nivoju. Prav tako bo potrebno določiti ustrezne ponderje, s pomočjo katerih bo mogoče kvantitativno vrednotiti stanje ogroženosti informacij v zdravstveni ustanovi. Za področje zdravstva bo potrebno določiti tudi, kaj so sprejemljivi sistemski ukrepi za konkretne probleme s področja varnosti informacij. Takšno institucijo potrebujemo torej še preden bo lahko prva ZO vstopila v postopek potrjevanja skladnosti s standardom.
S stališča odgovornosti ob morebitni zlorabi, poškodovanju ali nedostopnosti podatkov ostaja za to odgovorna sama ZO. Organizacija, ki bi bodisi certificirala ZO oz. preverila njeno skladnost, pri tem ne nosi nobene odgovornosti, saj izdata le dokument, ki potrjuje, da se v preverjeni organizaciji prizadevajo za ohranitev zaupnosti, celovitosti in razpoložljivosti informacij.
Poslovni razlogi za zagotavljanje skladnosti
Varovanje informacij je pomembna aktivnost vsake sodobne organizacije. Razumljivo je, da si tudi vodstvo ZO prizadeva, da informacijski tokovi delujejo in da pri tem niso ogroženi poslovni interesi. Po izkušnjah podjetij/organizacij, ki so uvedle enega od standardov kakovosti (npr. ISO 9001, ISO 14000 ali ISO 18000), je proces uvajanja in zagotavljanja skladnosti z določili standarda pomenil bistveno spremembo v delovnih procesih. Podoben korak pomeni tudi zagotavljanje sprejemljive informacijske varnosti z uporabo preverjenega modela, ki ga daje mednarodni standard ISO17799. Upravljanje z varnostjo informacij v podjetju oz. ustanovi postane dokumentiran vodstven proces, kar pomeni, da je varnost informacij mogoče poslej upravljati.
Za menedžment ZO bodo pomembni pozitivni učinki take strateške spremembe. V nadaljevanju je navedenih nekaj možnih neposrednih in posrednih koristi za ZO, ki opravičujejo naložbo v sistem upravljanje z varnostjo informacij:
1.	izboljšano varovanje pacientovih in lastnih informacij kot lastnine
2.	poenostavljeno poslovanje z notranjimi (pacienti, dobavitelji, lekarne...) in tujimi poslovnimi partnerji (tuji pacienti, naši pacienti na tujem)
3.	povečan ugled in zaupanje pri pacientih, poslovnih partnerjih in v javnosti
4.	obvladovanje poslovnih tveganj, ki jih prinašajo varnostni incidenti
5.	zmanjševanje učinkov varnostnih incidentov ter enostavnejše zagotavljanje neprekinjenega delovnega procesa
6.
finančne koristi zaradi zmanjšane posredne škode ob varnostnem incidentu
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7.	učinkovitejše investiranje v opremo in postopke za zagotavljanje večje informacijske varnosti
8.	izpolnjevanje zakonsko naloženih obveznosti in zahtev EU glede varovanja podatkov/informacij
Eden izmed razlogov je gotovo tudi zagotavljanje ustrezne elektronske komunikacije med ZO in organizacijami, ki bi bile uradno certificirane npr. farmacevtske organizacije, drugi dobavitelji, državna uprava. Pričakovati je, da bodo ZO v bodoče morale zagotavljati ustrezno stopnjo varovanja informacij zaradi povezovanja z zunanjimi organizacijami. Medsebojno zaupanje je pogoj za vzpostavitev komuniciranja med IS organizacij. To zaupanje najlaže vzpostavimo, če so organizacije med seboj primerljive glede varnostne politike, pogoj za to pa je podrejanje istim varnostnim standardom.
Koraki do zagotovljene skladnosti s standardom ISO17799
Koraki do želene varnosti informacij v slovenskem zdravstvu z zagotavljanjem skladnosti s standardom ISO17799 v ZO bi bili naslednji:
1.	zagotoviti podporo Ministrstva za zdravje celotnemu procesu upravljanja z varnostjo
2.	pridobiti podporo projektu s strani strokovnih institucij s področja zdravstvene informatike
3.	v posameznih ZO bi morala vodstva sprejeti odločitev, da želijo zagotoviti varnost informacij v skladu s tem standardom
4.	doseči bi morali konsenz mnenj in interesov ZO v strokovnih združenjih, v katera so vključene ZO
5.	ZO morajo zagotoviti finančna sredstva za projekt
6.	zagon projekta
7.	močna podpora vodstva ZO notranjim izvajalcem projekta
8.	sodelovanje z zunanjimi strokovnjaki
Za spremembo stanja na področju varnosti informacij v ZO bo pomembna tudi ustrezna kadrovska politika. Ker bo zagotavljanje skladnosti trajen multidisciplinarni projekt, ki bo vključeval vsa področja delovanja ZO, bodo ključno vlogo nosili izkušeni notranji strokovnjaki v sodelovanju z zunanjimi sodelavci. Zato bo morala ZO zagotoviti lastne usposobljene kadre, ki bodo s pomočjo zunanjih sodelavcev pripravili politiko varovanja informacij ter jo nato sami implementirali in izboljševali.
Seveda lahko pričakujemo, da bomo pri izvajanju postopkov zagotavljanja skladnosti s standardom naleteli na odpor znotraj ZO. Kot kažejo izkušnje pri uvajanju drugih standardov na področju gospodarstva, so ovire organizacijske, finančne in človeške, ne glede na to, kateri standard se uvaja. Vsak prinaša spremembe za zaposlene, ki lahko pomenijo večje zahteve do posameznika, s tem pa "poslabšanje ugodja" na delovnem mestu.
Zaključek
Za slovensko zdravstvo je korak k zagotavljanju večje varnosti informacij nujen. Najučinkovitejša pot bo z zagotavljanjem skladnosti po merilih standarda kot je ISO17799.
V strokovnih krogih informatikov v zdravstvu skoraj nihče več ne dvomi o ustreznosti tega standarda za upravljanje z varnostjo v ZO, le o tem, kako se tega lotiti, so mnenja še deljena.
Upravljanje z varnostjo informacij bo za vsako ZO trajen proces, ki se podobno kot procesi
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Rudel D: Upravljanje z varnostjo informacij v zdravstvenih organizacijah
zagotavljanja kakovosti izdelkov v gospodarstvu, nikoli ne konča.
Literatura
1.	Zdravniška zbornica Slovenije: Kodeks medicinske deontologije. http://www.zzs-mcs.si/kodeks, 1997.
2.	Zakon o zdravstvenem varstvu in zdravstvenem zavarovanju. UL RS 9/92 in spremembe.
3.	Zakon o zdravstveni dejavnosti. UL RS 9/92 in spremembe.
4.	Zakon o zbirkah podatkov s področja zdravstvenega varstva. UL RS 5/00.
5.	Widenet: Prorec.si. http://www.drustvo-sdmi.si,
2005.
6.	Vlada RS: Projekt razvoja upravljanja sistema zdravstvenega varstva. http://www2.gov.si/mz/hsmp/hsmp.nsf.
7.	British Standard Institute: Information technology - Code of practice for information security
management. BS ISO/IEC 17799:2000 (BS7799-1:2000).
8.	British Standard Institute: Information Security Management System - Specifications with guidance for use. BS7799-2:2002.
9.	Andrejaš B: Uvajanje sistema vodenja varovanja informacij v skladu s standardom BS 7799-2:2002 in certificiranje. Infosec, Nova Gorica, 2003.
10.	SIST Standard. PSIST BS7799. SIST 1998.
11.	UL RS 52/00, 6982.
12.	Hajtnik T: Zahteve Evropske unije in državne uprave za uvajanje ISO 17799 standarda. Palsit BS 7799 konferenca, Postojna, september 2004.
13.	Hajtnik T: Priporočila za pripravo informacijske varnostne politike. CVI, junij 2002.
14.	Ključevšek R: Varnost informacij po novem. Monitor. 2003; 13: 1. Supl. SISTEMI 2003, 14.
15.	Palsit d.o.o. http://www.palsit.com/, 2005.
16.	Danesa d.o.o. BS7799. http://www.bs-7799.org, 2005.
17.	Housing d.o.o. http://www.housing.si, 2005.
18.	CEN/TC Health Informatics. http://www.centc251 .org/TCMeet/doclist/TCdoc0 4/N04-044WGIII-Report-Berlin-2004.pdf, 2004.
19.	Sunderland NHS Health Portal. http://www.sunderland.nhs.uk/security/top, 2005.
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Strokovno-znanstveni prispevek ■
Označevanje in odkrivanje
pomenskih razmerij v medicinskih besedilih
Špela Vintar
Izvleček. Prispevek opisuje metodo samodejnega pomenskega označevanja medicinskih besedil s pojmi in pomenskimi razmerji metatezavra UMLS. Podlaga za takšno označevanje je jezikovna obdelava, pri kateri se v besedilo vnese osnovne ravni jezikoslovne analize, predvsem besedne vrste in osnovne oblike besed. V nadaljevanju opišemo dva načina izrabe tako obogatenih besedil, in sicer za namene medjezičnega iskanja dokumentov in za odkrivanje novih pomenskih razmerij v medicinskih besedilih.
Annotating and Discovering Semantic Relations in Medical Texts
Abstract. The paper describes a method of automatic semantic annotation of medical texts on the basis of the UMLS Metathesaurus. Prior to semantic processing the texts are linguistically analysed, lemmatised and tagged for part-of-speech. Two application areas are then outlined in more detail, first the exploitation of semantically annotated documents in Cross-Language Information Retrieval and second the discovery of new semantic relations in medical texts.
■ Infor Med Slov: 2005; 10(1): 9-18
Institucija avtorja: Filozofska fakulteta, Univerza v Ljubljani.
Kontaktna oseba: Špela Vintar, Filozofska fakulteta, Univerza v Ljubljani, Aškerčeva 2, 1000 Ljubljana. e-mail: spela.vintar@ff.uni-lj.si.
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Vintar Š: Označevanje in odkrivanje pomenskih razmerij v medicinskih besedilih
Uvod
Osnovna problematika širokih in visoko razvitih znanstvenih področij, kot je medicina, je v (ne)obvladovanju količine znanja, ki je nakopičeno v znanstvenih besedilih in člankih, pogosto zbranih v velike besedilne baze, kakršna je Medline. Računalniška obdelava jezika lahko pomembno pripomore k dostopanju do besedilnih podatkov, predvsem tako da zmanjšuje dvoumnost in variabilnost jezikovnih izrazov na vseh ravneh. Če denimo iščemo besedo terapija, nas navadno zanimajo tudi oblike besede terapije, terapiji itd., morda bi želeli obenem iskati tudi sopomenke, kot je zdravljenje, morda pa bi nas zanimala tudi besedila o iskanem pojmu v tujem jeziku, denimo angl. treatment, therapy.
Pri tradicionalnem iskanju podatkov (Information Retrieval) se za razreševanje oblikoslovne razvejanosti jezika uporablja krnjenje (stemming), postopek, ki različne oblike iste besede "obteše" do njim skupnega jedra ali krna. Nekoliko kompleksnejši postopek jezikovne obdelave je lematizacija oziroma opremljanje besed z njihovimi pravimi osnovnimi oblikami — lemami; zanj potrebujemo leksikon besed in njihovih oblik, pri dvoumnih besednih oblikah (npr. v slovenščini brez kot rodilnik množine samostalnika breza ali predlog brez) pa nam pomaga besednovrstno označevanje.
Če želimo iskati dokumente v večjezičnih besedilnih zbirkah, se soočamo s problemom, da jezik iskalne zahteve ni enak jeziku, v katerem so napisani — nekateri — dokumenti. Za premostitev jezikovne vrzeli se lahko uporabi strojni prevajalnik, ki prevede bodisi le iskalno zahtevo bodisi vse dokumente v zbirki. Druga možnost, ki jo v tem prispevku podrobneje opisujemo, pa je samodejno pomensko označevanje iskalnih zahtev in dokumentov.
Nujen vir za takšno označevanje je večjezični tezaver ali ontologija, se pravi hierarhično urejena baza pojmov določenega področja, ki posameznim strokovnim terminom priredi jezikovno neodvisno pojmovno oznako. Za področje medicine je tak vir
UMLS (Unified Medical Language System),a ki v svojem metatezavru opisuje prek milijon medicinskih pojmov in vsebuje več kot pet milijonov medicinskih izrazov v različnih jezikih. Če torej pomenski označevalnik v besedilih različnih jezikov poišče medicinske izraze in jim priredi jezikovno neodvisne pojmovne oznake, in se enak postopek uporabi tudi na sami iskalni zahtevi, to omogoča medjezično iskanje dokumentov.
Pričujoči prispevek v prvem delu opisuje prototip takega medjezičnega iskalnika, ki smo ga razvili za angleški in nemški jezik v okviru projekta MUCHMORE.b Predstavljeni so tudi rezultati evalvacije, kjer smo pojmovno usmerjeno metodo primerjali z drugimi znanimi metodami medjezičnega iskanja. V drugem delu spregovorimo še o raziskavah, kako izrabiti pomensko označena besedila za odkrivanje novega znanja, še posebej novih pomenskih razmerij med medicinskimi pojmi.
Pomensko označevanje za
•	I •	• V	• I	•
pojmovno medjezično iskanje
Temeljna naloga takšnega medjezičnega iskalnika je, da v besedilu oziroma iskalni zahtevi poišče termine in jih preslika na jezikovno neodvisno pojmovno raven. Kot vir terminologije in pojmovnih razmerij za področje medicine uporabljamo UMLS oziroma natančneje tri njegove komponente:
•	Specialist Lexicon, zakladnica besedišča, kjer so našteti posamezni izrazi skupaj z oblikoslovnimi značilnostmi, besednimi oblikami in lemami,
•	Metathesaurus, metatezaver, osrednji terminološki vir, ki združuje medicinske izraze iz prek 100 različnih virov in 17
a http://www.nlm.nih.gov/research/umls b http://www.muchmore.dfki.de
Informatica Medica Slovenica 2005; 10(1)
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jezikov. Vsakemu terminu je prirejena pojmovna koda (CUI — Concept Unique Identifier), ki različne jezikovne in terminološke variante povezuje v skupni pojem,
• Semantic Network, pomenska mreža, ki pojme združuje v 134 pomenskih kategorij (TUI — Type Unique Identifier) in med njimi opredeljuje 54 možnih pomenskih razmerij.
<umlsterms>
<umlsterm id = "t1" from="w5" to = "w5">
<concept id = "t1.1" cui = "C0019134" preferred = "Heparin" tui = "T118 T121 T123">
<msh code = "D9.203.698.373.400" /> </concept> </umlsterm>
<umlsterm id = "t2" from="w11" to = "w11">
<concept id = "t2.1" cui = "C0033107" preferred = "prevention & control" tui="T170" /> </umlsterm>
<umlsterm id = "t3" from="w13" to = "w13">
<concept id = "t3.2" cui = "C0039798" preferred = "therapeutic aspects" tui = "T169" /> </umlsterm>
<umlsterm id = "t4" from="w16" to = "w16">
<concept id = "t4.1" cui = "C0009566" preferred = "Complication" tui="T046" /> </umlsterm> </umlsterms> <semrels>
<semrel id = "r1" term1 = "t1.1" term2 = "t4.1" reltype = "diagnoses" /> <semrel id = "r2" term1 = "t4.1" term2 = "t1.1" reltype = "produces" /> <semrel id = "r3" term1 = "t1.1" term2 = "t4.1" reltype = "affects" /> </semrels>
Slika 1 Pomensko označeno besedilo.
Cilj projekta MUCHMORE je bil preveriti učinkovitost medjezičnega iskanja s pomočjo pomenskega označevanja in rezultate primerjati z drugimi uveljavljenimi metodami. Preskus smo izvajali za omejeno zbirko dokumentov, in sicer 9.000 povzetkov medicinskih člankov v angleškem in nemškem jeziku, ki smo jih pridobili s Springerjevega spletišča.c
Za jezikovno predobdelavo besedil smo uporabili več med seboj povezanih orodij, in sicer SPPC1 za tokenizacijo oziroma razčlembo besedila na stavke, besede in ločila, TnT2 za besednovrstno označevanje, Mmorph3 za oblikoslovno analizo in Chunkie4 za razpoznavanje besednih zvez.
Sledi pomensko označevanje, kjer se na ravni termina v besedilo vnesejo naslednji podatki:
•	pojmovna koda (CUI),
•	koda pomenske kategorije (TUI),
•	koda pripadajoče kategorije MeSH (Medical Subject Headings),
•	prednostni termin, tj. izraz, ki predstavlja priporočeno poimenovanje določenega pojma (Preferred Term).
c http://www.springerlink.de
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Vintar Š: Označevanje in odkrivanje pomenskih razmerij v medicinskih besedilih
Slika 2 Prikaz iskalne zahteve
z označenimi pojmi in razmerji.
Iskanje terminov v besedilu in označevanje s podatki iz UMLS-a temelji na oblikoslovni analizi, tako da se iščejo leme in ne besedne oblike. Prav tako se poiščejo večbesedni termini in termini, ki so skriti v zloženkah, kar je še posebej pomembno za nemščino. Sistem razpoznava tudi nekatere terminološke variacije, kot so zamenjan vrstni red besed v terminu ali tipične okrajšave. Poleg tega se v besedilu samodejno označijo možna pomenska razmerja med pojmi na ravni povedi, in sicer na podlagi razmerij, ki jih definira Semantic Network.
Za primer vzemimo naslednji angleški stavek: "For many decades, heparines have been used successfully for prophylaxis and treatment of thromboembolic complications world-wide".
V tej povedi najdemo medicinske izraze heparines, prophylaxis, treatment, thromboembolic, complications, ki jih označimo s ustreznimi podatki
iz UMLS-a. Nato preverimo vse možne kombinacije med najdenimi pomenskimi kategorijami (v tem primeru T118, T121, T123, T170, T169 in T046) in ugotovimo, da med pojmoma C0019134 Heparin in C0009566
Complication obstajata možni razmerji diagnoses in affects, ki ju prav tako označimo. Zapis teh podatkov v XML obliki kaže Slika 1.
Pri samodejnem pomenskem označevanju prihaja do dvoumnosti na več ravneh. Tako lahko isti termin priredimo več različnim pojmom, določeni pojmi pa imajo lahko več možnih pomenskih kategorij, kot je razvidno iz primera heparin. Prav tako je lahko dvoumna preslikava izbranega pojma v hierarhično strukturo MeSH, denimo anorexia, ki se ji lahko priredi oznaka C23.888.821.108 pod nadpomensko Signs and Simptoms, Digestive, ali F03.375.050 pod nadpomenkama Mental Disorders — Eating Disporders.
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Za preslikavo pojmov v strukturo MeSH smo se odločili predvsem z vidika pomenskih razmerij, saj so pomenske kategorije (TUI) in razmerja, ki jih opredeljuje UMLS, zelo splošni in zato pogosto preširoki za namene medjezičnega iskanja. Poleg tega smo nameravali v besedilih odkrivati nova pomenska razmerja, za to pa je drevesna struktura MeSH primernejša, saj je pri vsakem pojmu možno izbrati bolj ali manj specifično pomensko kategorijo.
Prototip medjezičnega iskalnika s pomočjo pojmov
Naša ciljna aplikacija je bila iskalnik po zbirki angleških in nemških medicinskih člankov, kjer je tipična iskalna zahteva v obliki elektronske pacientove kartoteke oziroma zdravnikove zabeležke o pregledu. Sistem je namenjen predvsem nemško govorečim uporabnikom, ki torej iskalno zahtevo sprožijo v nemščini, a jih zanima strokovna literatura v obeh jezikih. Preskusna različica je na voljo na naslovu http://muchmore.dfki.de.
Iskalna zahteva se sproti obdela z jezikovnega vidika, tako da je omogočeno boljše prepoznavanje terminov, nato pa sledi prej opisano pomensko označevanje. Po pomenski analizi sistem uporabniku ponudi pojme in možna razmerja med njimi, s katerimi lahko svojo medjezično iskalno zahtevo natančneje oblikuje. Če sistem v iskalni zahtevi kakega izraza ni našel, ga lahko uporabnik doda ročno v spodnje okence. Nato se sproži iskanje po zbirki dokumentov, ki prek pomenskih oznak poišče angleške in nemške zadetke.
Rezultati
Da bi ugotovili, ali pojmovni pristop v medjezičnem iskanju deluje bolje kot druge metode, smo izvedli niz preskusov. Za to smo uporabili že omenjeno zbirko medicinskih povzetkov v angleškem in nemškem jeziku in seznam 25 iskalnih zahtev, ki so nam jih posredovali medicinski strokovnjaki. Da lahko
ocenjujemo kakovost sistema, moramo poznati pravilni izbor dokumentov za vsako iskalno zahtevo. Ta izbor so prav tako opravili medicinski strokovnjaki, skupno število izbranih dokumentov pa je bilo 959. To število v nadaljevanju predstavlja 100-odstotni priklic, če poženemo vseh 25 iskalnih zahtev. Testne iskalne zahteve so večinoma kratke in jedrnate, denimo "Atrthroscopic treatment of cruciate ligament injuries" ali "Indication for implantable cardioverter defibrillator (ICD)".
Čeprav smo imeli na razpolago vzporedni angleško-nemški korpus, se pri evaluaciji medjezičnega iskanja pretvarjamo, da temu ni tako. Namesto tega smo poskušali prek iskalnih zahtev v nemščini dostopati do angleških dokumentov.
Najprimitivnejši pristop k medjezičnemu iskanju je, da z besedami nemške iskalne zahteve skušamo neposredno poiskati angleške dokumente. Utemeljitev tega pristopa je, da bo že spričo delnega prekrivanja strokovne terminologije v obeh jezikih mogoče najti ustrezne dokumente. Zares se pokaže, da na ta način "uganemo" 66 ustreznih dokumentov (glej vrstico DE2EN-token v Tabeli 1). Najbolje so se odrezale iskalne zahteve, ki so vsebovale kratico HIV ali latinske izraze, npr. diabetes mellitus.
Kot drugo primerjavo smo preskusili strojno prevajanje iskalnih zahtev iz nemščine v angleščino s komercialnim prevajalnikom PersonalTranslator 2001 (Linguatec, München). Čeprav je bilo v prevajalniku mogoče izbrati strokovno besedišče medicine in kemije, se je pokazalo, da program mnogih medicinskih izrazov ni zmogel prevesti, ker jih ni imel v slovarju. Pri drugih izrazih prevod ni bil mogoč, ker program ne zna analizirati zloženk. Tako se pri besedi Myokardinfarkt tudi Infarkt ni prevedel, čeprav ga ima v slovarju. Spet druge iskalne zahteve so se s strojnim prevajalnikom prevedle brezhibno, denimo:
• DE: Möglichkeiten der Korrektur von Deformitäten in der Orthopädie
14
Vintar Š: Označevanje in odkrivanje pomenskih razmerij v medicinskih besedilih
• EN: Possibilities of the correction of deformities in orthopedics.
Vrstica DE2EN-MT v tabeli kaže, da smo s tem pristopom našli 376 ustreznih dokumentov.
Nato smo te rezultate primerjali z rezultati, ki jih dobimo, če medjezikovno povezavo ustvarimo s pomočjo pojmovnih oznak iz UMLS-a. V teh poskusih se torej v nemški iskalni zahtevi poiščejo vsi medicinski termini, ki se jim priredijo pojmovne oznake iz Metathesaurusa (DE2EN-CUI), pojmovne oznake iz hierarhije MeSH (DE2EN-MeSH), na koncu pa se poiščejo še pomenska razmerja med njimi (DE2EN-SemRel). Prek teh oznak se išče ustrezne dokumente v angleški zbirki. Vrstica DE2EN-all-sem v tabeli kaže rezultat, ki ga dobimo, če pri iskalni zahtevi upoštevamo vse pomenske informacije skupaj. Kot je razvidno, pri slednjem dobimo tudi najboljši rezultat v sklopu pojmovno usmerjenih preskusov.5
Tabela 1 Rezultati medjezičnega iskanja.
	mAvP	št.dok. AvP 0.1		P10
DE2EN-token	0.0512	66	0.1530	0.1160
DE2EN-MT	0.1184	376	0.3382	0.2520
DE2EN-CUI	0.1620	366	0.3724	0.2800
DE2EN-MeSH	0.1699	304	0.3888	0.2600
DE2EN-SemRel	0.0229	23	0.0657	0.0480
DE2EN-all-sem	0.1774	404	0.3872	0.2720
DE2EN-SimThes	0.2290	409	0.4492	0.3640
DE2EN-all	0.2955	518	0.5761	0.4600
Da bi bila slika popolna, smo preskusili še tretjo metodo, ki na področju iskanja podatkov velja za najbolj obetavno, in sicer slovar podobnih izrazov (Similarity Thesaurus). Ta vsebuje samostalnike, samostalniške zveze, pridevnike in glagole, ki se v našem korpusu najpogosteje pojavljajo v sorodnih kontekstih in tako sklepamo, da imajo soroden pomen. Tak slovar je mogoče zgraditi tudi iz enojezičnega korpusa in ga uporabljati pri enojezičnem iskanju podatkov, v našem primeru pa smo ga zgradili na podlagi vzporednega korpusa. Tako pridobljeni slovar podobnih izrazov je za vsako nemško besedo vseboval množico najverjetnejših prevodnih ustreznic in ostalih
sorodnih besed. Te angleške besede so se uporabile za iskanje ustreznih angleških dokumentov. Spodnji primer kaže seznam angleških besed, ki jih slovar podobnih izrazov navaja za nemški Myokardinfarkt: infarction, acute myocardial infarction, mycardial, thrombolytic, acute, thrombolysis, crs, synchronisation, cardiogenic shock, ptca.
Poleg števila ustreznih dokumentov, ki ga navaja tretji stolpec Tabele 1, merimo učinkovitost našega sistema z ustaljenimi metodami projektov TREC.6 Drugi stolpec tako vsebuje splošno natančnost sistema, ki se računa kot srednja povprečna natančnost (mean average precision -mAvP). Natančnost pri tem pomeni razmerje med pravilno izbranimi dokumenti in vsemi izbranimi dokumenti. Predzadnji stolpec kaže povprečno natančnost ob priklicu 0,1 (AvP 0,1), saj Eichmann in soavtorji7 ugotavljajo, da je učinkovitost sistema bolje meriti v območju visoke natančnosti, s predpostavko, da uporabnike predvsem zanimajo visoko uvrščeni zadetki. Zadnji stolpec v skladu s tem navaja natančnost za 10 najvišje uvrščenih dokumentov (P10).
Kot komentar k prikazanim rezultatom povejmo, da nas je v okviru opisanih eksperimentov zanimalo predvsem, ali je s pomočjo pomenskega označevanja mogoče prekositi doslej uveljavljene metode s strojnim prevajanjem in s slovarjem sorodnih besed. Pokazalo se je, da nobena od posameznih pomenskih kategorij (CUI, MeSH ali SemRel) ni dovolj zanesljiva, da bi sama po sebi pomagala izbrati ustrezne dokumente v drugem jeziku, pač pa se zelo dobro odreže kombinacija vseh pomenskih kategorij (all-sem).
Metoda s slovarjem sorodnih besed sicer da boljši rezultat (SimThes), vendar jo moramo upoštevati z rezervo, saj smo slovar zgradili iz istega vzporednega korpusa, na katerem so potekali preskusi. Tako je bil slovar pravzaprav preveč ukrojen po meri, tako da v realnih situacijah medjezičnega iskanja takih rezultatov ne bi mogli ponoviti. Zanimivo je, da kombinacija slovarja sorodnih besed s pomenskimi kategorijami da najboljše rezultate od vseh (all), kar znova potrjuje
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koristnost slednjih. Seveda pa se je ob tem treba zavedati, da je področje medicine izjemno v smislu opremljenosti s terminološko-ontološkimi viri — čeravno tu slovenščina še precej zaostaja — in da si podobnih aplikacij na drugih področjih ne moremo zamišljati brez predhodnega vložka v (nad)gradnjo teh virov.
Odkrivanje pomenskih
• • ■ • • •
razmerij v medicini
V nadaljevanju prispevka se posvečamo drugi, bolj raziskovalno usmerjeni aplikaciji pomenskega označevanja, in sicer odkrivanju novega znanja in novih povezav med znanimi dejstvi. Tu smo se posebej osredotočili na pomenska razmerja na področju medicine, pri čemer se je motivacija za pričujočo raziskavo porodila iz opažanj, da projekcija pomenskih razmerij iz UMLS-a na besedilo, se pravi tistih, ki jih med pomenskimi kategorijami (Semantic Type) opredeljuje Semantic Network, redko ustreza dejanskim pomenskim razmerjem, ki so udejanjena v besedilu.
Razlogov za to je več. Semantic Network definira skupno 54 pomenskih razmerij, specifičnih za področje medicine, kot so location_of, affects, treats, causes, interacts_with. Ta razmerja se ne nanašajo na konkretne pojme, ampak so opredeljena na ravni pomenskih kategorij, na primer Pharmacologic Substance - affects - Cell Function. Če ta zelo splošna razmerja, ki seveda držijo le za omejeno podmnožico družine farmakoloških pripravkov in celičnih funkcij, avtomatsko preslikamo na dokumente, številna ugotovljena razmerja ne držijo. Tako se denimo v stavku pojavita pojma discectomy in history, ki ju uvrstimo v pomenski kategoriji Therapeutic Procedure in Occupation or Discipline. Med tema dvema kategorijama UMLS definira razmerje method_of, vendar v našem primeru enačba discectomy = method_of history ne drži.
različnimi, celo nasprotnimi razmerji, kot kaže spodnji primer:
Therapeutic Procedure Neoplastic Process
Therapeutic Procedure Neoplastic Process
Therapeutic Procedure Neoplastic Process
Therapeutic Procedure Neoplastic Process
Therapeutic Procedure | Neoplastic Process
prevents | complicates | affects | treats |
associated with
Želeli smo torej ugotoviti, ali je s pomočjo vseh ostalih podatkov, ki jih imamo na razpolago, se pravi jezikoslovne analize, pojmovnih oznak (CUI) in oznak iz MeSH-a, mogoče razlikovati dejanska razmerja od hipotetičnih in ali se dejanska razmerja v besedilih izražajo na ustaljene načine.
Sorodne raziskave
Da je s pomočjo statistične obdelave medicinskih besedil mogoče priti do novih in koristnih hipotez, je prvi pokazal že Swanson.8 Njegove zamisli so naprej razvijali Weeber in soavtorji,9 ki opisujejo naprednejši model obdelave medicinskih besedil z jezikovno in pomensko komponento. Večina raziskovalcev se s to temo ukvarja v zvezi s samodejno izdelavo ontologij, zato se v besedilih predvsem išče taksonomske povezave med pojmi, se pravi razmerja hipo- in hipernimije. Maedche in Staab,10 denimo, razvijata metodo učenja asociativnih pravil za iskanje pojmovnih mrež. Za razliko od omenjenih pristopov se je naša raziskava usmerila v odkrivanje znanih, za medicino tipičnih razmerij, vendar brez uporabe razmerij, ki jih opredeljuje Semantic Network.
Da je stvar še bolj meglena, sta dve pomenski kategoriji teoretično lahko povezani s številnimi
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Vintar Š: Označevanje in odkrivanje pomenskih razmerij v medicinskih besedilih
Razlikovanje med dejanskimi in hipotetičnimi razmerji
Cilj postopka je presejati samodejno označena pomenska razmerja, ki bi lahko držala med pomenskimi kategorijami, in izmed njih izbrati tista, ki so v besedilu res izražena. Izhajamo iz dveh predpostavk:
•	pomenska razmerja se v besedilu udejanjajo z določenimi jezikovnimi sredstvi, predvsem z glagoli, in
•	pomenska razmerja so bolj pomembna, če se pojavijo med dvema pomembnima, tj. čimbolj specializiranima, pojmoma.
V ta namen smo preverili sopojavljanje posameznih razmerij s posameznimi glagoli v korpusu in za vsak glagol zbrali seznam petih najbolj tipičnih razmerij, ki jih lahko označuje, kot kaže spodnji primer:
•	activate (84)
•	interacts_with (1937)
•	produces (836)
•	affects (544)
•	disrupts (324)
•	result_of (295)
Obenem smo v duhu druge predpostavke pojme obtežili s statistično vrednostjo IDF (Inverse Document Frequency), ki višje uvršča pojme, ki se pojavljajo le v omejenem številu dokumentov, nižje pa splošne pojme, kot so patient, therapy, result itd. Obe metodi smo združili in uporabili kot sito za izbiro dejanskih in pomembnih razmerij. Po uporabi obeh sit v besedilu ostane približno 31% samodejno označenih razmerij.
Odkrivanje novih pomenskih razmerij
Ob pregledovanju samodejno označenih pomenskih razmerij pred in po situ smo opazili, da številna razmerja še vedno ostajajo neopažena, ker niso zapisana v UMLS-u. Poleg tega se za namene odkrivanja znanja pokaže, da je 54 pomenskih razmerij, kot jih opredeljuje Semantic Network, pravzaprav preveč, saj so si številna med njimi zelo podobna (npr. interacts_with in associated_with). Tako smo se odločili, da namesto vseh 54 razmerij poskušamo odkrivati le 15 najpogostejših.
Izhajamo iz predpostavke, da pogosto sopojavljanje dveh medicinskih pojmov v besedilu pomeni, da sta pojma med seboj nekako povezana. Ce bi skušali ugotavljati pomensko razmerje za vsak par pojmov posebej, bi imeli na eni strani preveč takih pojmovnih parov, na drugi strani pa premajhne pogostosti njihovih sopojavitev, da bi na tej podlagi lahko ugotovili pomensko razmerje. Pojme zato prevedemo na njihovo splošnejšo obliko, in sicer kategorijo MeSH drugega reda.
V drevesni strukturi MeSH-a so osnovne veje označene s črkami, npr. A - Anatomy, B -Organisms, C - Diseases. Na naslednji stopnji se ta delijo v bolj specializirane skupine pojmov, denimo B05 - Fungi ali C02 - Virus Diseases. Predpostavljamo torej, da pogosto sopojavljanje dveh podkategorij MeSH (npr. A0^C23) pomeni, da med njima obstaja pomensko razmerje.
Za vsako od izbranih 15 razmerij smo izdelali seznam 300 najpogostejših parov podkategorij MeSH:
• treats - D27|C23, D3|C23, E7|C23, E7|C2 ...
Nato v korpusu označimo vsa domnevna razmerja, ki ustrezajo temu seznamu, izločimo pa tista, ki vključujejo preveč splošne termine.
Uspešnost te metode smo nato preskusili na dva načina. Pri prvem nas je zanimalo, kako izboljšanje kakovosti pomenskih razmerij oziroma dodajanje
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novih razmerij vpliva na pojmovno iskanje podatkov.
Izdelali smo pet različic korpusa in iskalnih zahtev. Pri prvi smo iz obstoječih samodejno označenih pomenskih razmerij odstranili vsa tista, ki so vključevala preveč splošne pojme (UMLS-IDF), pri drugi smo poleg prvih odstranili tudi vsa razmerja, ki niso vsebovala tipičnih glagolov (UMLS-IDF-glag). Nato smo v tako prečiščeni korpus dodali novo odkrita razmerja (UMLS-IDF-glag), poleg tega pa smo novo odkrita razmerja dodali tudi prvotnemu neprečiščenemu korpusu (UMLS+nove). Nazadnje smo korpus označili le z novo odkritimi razmerji (samo nove).
Tabela 2 Evalvacija izbiranja in odkrivanja pomenskih razmerij z medjezičnim iskanjem.
	mAvP	št. dok.	AvP 0.1	P10
UMLS-IDF	0,126	203	0,315	0,280
UMLS-IDF-glag	0,107	175	0,282	0,264
UMLS-IDF- glag+nove	0,124	197	0,336	0,308
UMLS+nove	0,153	259	0,419	0,344
samo nove	0,116	213	0,363	0,280
razmerij. Kljub temu primerjava našega prečiščenega in obogatenega korpusa UMLS-IDF-glag+nove kaže 63% ujemanje z ročno označenimi razmerji, kar je spodbuden rezultat.12
Razprava
Samodejna pomenska analiza besedil s pomočjo ontologije, ki jo opisujemo v pričujočem prispevku, je pomembna raziskovalna veja ne le v kontekstu medjezičnega iskanja, ampak tudi kot pomembna tehnologija v okviru semantičnega spleta. Pri opisani raziskavi in predstavljenih poskusih pa smo imeli v prvi vrsti pred očmi uporabniški vidik, se pravi omogočanje učinkovitejšega dostopa do relevantnih informacij prek jezikovnih meja.
Medtem ko je mogoče trditi, da so rezultati našega pojmovno obogatenega medjezičnega iskanja na medicinskem področju vidna izboljšava dosedanjih zabeleženih rezultatov, so izsledki pri odkrivanju pomenskih razmerij šele na poti k dejanski uporabnosti. Nadaljnje raziskave vodijo predvsem k metodam strojnega učenja in vizualizacije
podatkov.13
Rezultati v Tabeli 2 so za medjezično iskanje seveda vsi po vrsti slabi, vendar jih moramo razumeti zgolj kot preskus uspešnosti naše metode izbiranja in odkrivanja pomenskih razmerij. Iz njih precej jasno izhaja, da najboljši rezultat dobimo, če uporabimo čimveč podatkov, se pravi razmerja iz UMLS-a in zraven še novo odkrita razmerja. To je v skladu z ugotovitvami številnih avtorjev,11 da je pri iskanju podatkov širjenje iskalne zahteve skoraj zmeraj uspešnejša strategija od oženja ali natančnejše opredelitve iskalne zahteve, čeprav se to morda na prvi pogled ne zdi logično.
Druga faza evalvacije je zajemala primerjavo naših metod sejanja in odkrivanja razmerij z besedili, kjer so pojme in razmerja ročno označili medicinski strokovnjaki. Žal smo imeli na razpolago omejeno število takšnih besedil, nadaljnja težava pa je bila dejstvo, da se pri svojem označevanju strokovnjaki niso omejili na istih 15
Da je pojmovno obogateno medjezično iskanje dokumentov za uporabnike izredno dobrodošla in koristna aplikacija, se je pokazalo v fazi zaključne evalvacije projekta Muchmore, kjer so iskalnik ocenjevali angleško in nemško govoreči medicinski strokovnjaki.14 Povprečna skupna ocena iskalnika je dosegla 3,40 (na lestvici od 1 do 5, kjer je 5 najvišja ocena), vendar so se uporabniki strinjali, da je način iskanja na podlagi pacientove kartoteke in pojmovne analize izredno zanimiv in bi v prihodnosti utegnil nadomestiti klasično iskanje s ključnimi besedami. Po drugi strani pa nedvomno drži, da bo do prave premostitve jezikovnih preprek moralo preteči še precej raziskovalne vode. Še posebej, če naj bi rešitve vključevale tudi slovenščino.
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Vintar Š: Označevanje in odkrivanje pomenskih razmerij v medicinskih besedilih
Literatura
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3.	Petitpierre D, Russel G: MMORPH - The Multext Morphology Program. Multext deliverable report for the task 2.3.1, ISSCO, University of Geneva, Switzerland, 1995.
4.	Skut W, Brants T: A maximum entropy partial parser for unrestricted text. Proceedings of the 6th ACL Workshop on Very Large Corpora (WVLC), Montral, Canada, 1998.
5.	Volk M, Ripplinger B, Vintar S, Buitelaar P, Raileanu D, Sacaleanu B: Semantic Annotation for Concept-Based Cross-Language Medical Information Retrieval. International Journal of Medical Informatics, Volume 67:1-3, December 2002.
6.	Gaussier E, Grefenstette G, Hull DA, Schulze BM: Xerox TREC-6 site report: Cross language text retrieval. Proceedings of the Sixth TExt Retrieval Conference (TREC-6). National Institute of Standards Technology (NIST), Gaithersburg, MD, 1998.
7.	Eichmann D, Ruiz M, Srinivasan P. Cross-Language Information Retrieval with the UMLS Metathesaurus. Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, 1998.
8.	Swanson DR, Smalheiser NR: An interactive system for finding complementary literatures: A stimulus to scientific discovery. Artificial
Intelligence 1997, 91:183-203.
9.	Weeber M, Klein H, Aronson JG, Mork L, Jong van den Berg, Vos R: Text-Based Discovery in Biomedicine: The architecture of the DAD-System. The American Medical Informatics Association 2000 Symposium.
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12.	Vintar Š, Buitelaar P, Volk M: Semantic Relations in Concept-Based Cross-Language Medical Information Retrieval. Proceedings of the ECML/PKDD Workshop on Adaptive Text Extraction and Mining (ATEM), 2003, Cavtat-Dubrovnik, Croatia.
13.	Vintar S, Todorovski L, Sonntag D, Buitelaar P: Evaluating Context Features for Medical Relation Mining. Proceedings of the European Workshop on Data Mining and Text Mining for Bioinformatics, held in conjunction with ECML/PKDD, 2003, Dubrovnik, Croatia.
14.	Report on User Evaluation, Deliverable 10.1 of the IST-1999-11438 project Muchmore, http://muchmore.dfki.de.
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Strokovno-znanstveni prispevek ■
Zdrave povezave
Healthy Connections
Vicki Powers
Izvleček. Cilji: Zdravstvena industrija je močno odvisna od informacij . Zahteva sodelovanje številnih strani, zato lahko omrežna tehnologija izboljša kakovost, zmanjša stroške in podpre aplikacije, ki izboljšujejo oskrbo pacientov. Strategije: Omrežna tehnologija, ki pomeni povezovanje podatkovnih, video in avdio vsebin, je najboljša priložnost za izboljševanje učinkovitosti in kakovosti oskrbe. Rezultat: Dostop do informacij oddaljenih lokacij prek ožičenih ali brezžičnih povezav v realnem času. Prihodnost zdravstva je odvisna od povezovanja zdravstvenih informacij in sinergije znanja vseh vpletenih strani.
Abstract. Objectives: The healthcare industry strongly depends on information and requires cooperation of many clients. Therefore, it can use network technology to increase quality, decrease costs and support network applications for patient care improvement. Strategies: Network technology — integration of voice, video, and audio solutions — is the best way to increase efficiency, healthcare quality and medical treatment results. Results: These solutions offer real time access to information through wireless connectivity and remote locations. The future of healthcare depends on medical knowledge integration of all parties involved.
■ Infor Med Slov: 2005; 10(1): 19-25
Institucija avtorja: Cisco Sytems.
Kontaktna oseba: Matjaž Savnik, Cisco Systems, Dunajska 156, 1000 Ljubljana. email: msavnik@cisco.com.
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Powers V: Zdrave povezave
Sodelovanje tehnologije s ponudniki zdravstvenih storitev zagotavlja dobrobit družbe in varnost bolnikov
Zdravstvena skupnost je bolj kot katerakoli druga industrija ali segment odvisna od prenosa informacij — podatkov o vsem: od zdravstvenega kartona pacienta do opozoril o mogočih medsebojnih vplivih med zdravili in rezultatov laboratorijskih preiskav do najbolj natančnih in aktualnih medicinskih spoznanj.
Informacijska tehnologija predstavlja odlično priložnost za izboljšanje kakovosti, učinkovitosti in zmanjšanje stroškov s pomočjo večje natančnosti, zanesljivosti in hitrosti komunikacij med ponudniki zdravstvenih storitev in bolniki — pa tudi komunikacijo med izvajalci zdravstvenih storitev.
Slika 1 IT olajša že prvi stik s pacientom.
Omrežna tehnologija ima doslej najpomembnejšo vlogo pri dostopu in prenosu informacij za ponudnike zdravstvenih storitev. Informacijska tehnologija zdravnikom omogoča bolj učinkovito sodelovanje, saj so z njeno pomočjo bolje opremljeni za dostop do informacij ob pravem času in na pravem kraju. Na isti način lahko tudi izboljšajo pretok dela in končno tudi zagotovijo svojim bolnikom učinkovito klinično okolje in nego. Omrežna tehnologija še posebej omogoča dostop do novih aplikacij, kot so na primer
elektronske zdravstvene kartoteke (electronic health records — EHRs) in elektronskih receptov, s pomočjo katerih lahko bistveno izboljšamo oskrbo bolnikov in komunikacijo z njimi. "Mobilnost in informacije na določeni točki oskrbe postajajo čedalje bolj pomembne za izvajanje oskrbe," pravi John Nebergall, podpredsednik oddelka za spletne recepte pri podjetju Allscripts, ponudniku kliničnih programskih rešitev. "Danes ima le 15 do 20 odstotkov zdravnikov v Združenih državah dostop do elektronskih zdravstvenih kartotek, pričakujemo pa, da bo to število doseglo 50 odstotkov v naslednjih 18 do 24 mesecih."
Nove potrebe, nova orodja
Povezovanje ponudnikov storitev z uporabo omrežja
Na dinamično rast informacijske tehnologije v sodobnem zdravstvu vpliva več dejavnikov; še posebej pa plačniki, ki zahtevajo kakovostna poročila, obvladovanje stroškov in učinkovitosti, ter večja raven vladnega zanimanja za zdravstvo. Končni cilj dr. Brailerja, nacionalnega koordinatorja za zdravstveno IT tehnologijo, je "družbeno zdravstveno omrežje", ki s pomočjo omrežne tehnologije povezuje zdravnike, sestre, radiologe in ostale izvajalce zdravstvenih storitev v bolnišnicah, klinikah in drugod.
"Zavedamo se, da informacijska tehnologija zagotavlja večjo izbiro med različnimi načini zdravljenja za bolnike ter hkrati omogoča boljšo in stroškovno bolj učinkovito oskrbo," je povedal dr. Brailer v svojem nedavnem govoru. "Tehnologija namreč podpira ponudnike storitev in strokovnjake ter jim pomaga. Na tak način lahko ti ljudje resnično spremenijo življenja Američanov. Zdravstvena IT ne izboljša samo kakovosti življenja, temveč tudi zagotavlja večji izkoristek investicije v zdravstvo. S tem pomaga ZDA, da postanejo bolj konkurenčne — istočasno lahko poviša produktivnost in življenjski standard."
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Slika 2 Povezovanje z najrazličnejšimi aplikacijami.
Trenutno so v teku prizadevanja, da dosežejo oba cilja, vendar pa je dela še veliko. "Klinični zdravniki res uporabljajo elektronske zdravstvene kartoteke, vendar pa jih nekateri zdravniki sprejemajo hitreje kot drugi, zato med njimi nastaja razkorak, ki je odvisen od velikosti dejavnosti," trdi dr. Brailer. "Večje ordinacije — že zaradi svoje velikosti — imajo več sredstev in boljšo pozicijo za uvajanje informacijske tehnologije ter lažje ublažijo tveganja... Če pa predpostavljamo, da elektronske zdravstvene kartoteke izboljšujejo zdravstveni status — kakor nam kažejo dokazi — potem je naša dolžnost, da vsem ordinacijam in bolnišnicam omogočimo, da uporabijo ta življenjsko pomembna orodja."
Kljub izzivom, s katerimi se soočajo manjše ordinacije, pa mnogi napredujejo k cilju — integracija informacij in komunikacij z uporabo inovativne tehnologije preko specifičnih aplikacij, vključno z EHR sistemi, brezžičnimi povezavami, video konferencami in elektronskimi recepti.
Večja učinkovitost
Uvajanje spletnih postopkov na kliniki Suncoast Medical Clinic
Po besedah Josha Adlerja, izvršnega direktorja pri Suncoast Medical Clinic, Florida, ZDA, so stroškovne neučinkovitosti v zdravstvenem sistemu najbolj skrb vzbujajoče področje za sodobne ponudnike storitev. Klinika Suncoast,
kjer na sedežu podjetja ter petih satelitskih lokacijah dela 53 zdravnikov in 21 specialistov, je trenutno v fazi uvajanja elektronskih postopkov, s pomočjo katerih bodo lahko opravljali klinične in administrativne naloge in tako odpravili ročno zapisovanje ter izboljšali splošno kakovost oskrbe. S kliniko je sodelovalo podjetje Threadfin Business Solutions, ki je registriran partner podjetja Cisco. Threadfin je kot neodvisen analitik priporočilo ustrezno omrežno infrastrukturo, ki bo zadostila sedanjim in prihodnjim tehnološkim zahtevam klinike.
Slika 3 Brezvrvični IP telefon.
Adler verjame, da zdravniki morajo spoznati zdravstveno tehnologijo. Industrijski strokovnjaki vključno z Adlerjem iščejo načine za premagovanje trenutnih izzivov, ki vključujejo:
•	večji poudarek na kazalce kakovosti,
•	spremembe pri uporabi bolnišničnih storitev,
•	nadzor skladnosti z relevantnimi pravilniki,
•	pomen nadomestil pri plačilu za storitve,
•	dostop varnih omrežij za mobilnost izvajalcev storitev.
"Menim, da s tehnologijo zdravniki lahko delajo brez težav," pravi Adler. "Tehnologija je ogromna
22
Powers V: Zdrave povezave
sila v učinkovitosti in boljši negi bolnikov. Zdravnikom omogoča, da svojim pacientom zagotovijo boljšo oskrbo, saj imajo dostop do podatkov v realnem času, kar je bistvenega pomena pri zdravljenju."
Ko je Adler prispel na kliniko Suncoast leta 2004, se je osredotočil na to, da je zbral najboljše zdravnike in tako ustvaril okolje, ki pozitivno vpliva na oskrbo bolnikov. Po Adlerjevem mnenju je prav dostop do informacij tista kritična komponenta, ki to omogoča. Vsa tehnološka prizadevanja na kliniki Suncoast podpirajo njeno temeljno nalogo: ustvariti karseda učinkovito okolje za zdravljenje pacientov, kar posledično omogoča bolje rezultate.
Leta 2004 so na kliniki nadgradili tehnološko infrastrukturo, tako da sedaj podpira omrežne aplikacije, s pomočjo katerih so digitalizirali delovne postopke, kot so človeški viri, nadzorovana oskrba, zaračunavanje in zbiranje, zapisovanje in upravljanje s podatki. Adler zdravnike na kliniki opisuje kot napredno skupino, ki dobro pozna tehnologijo in jo z veseljem uporablja. Ponavadi medicinske skupine ne zapravljajo rade denarja, v tem primeru pa so partnerji klinike Suncoast enoglasno izglasovali nadaljnje investicije v tehnologijo.
Suncoast trenutno uvaja laboratorijski informacijski sistem (LIS), ki bo povezoval različne prostore, za osnovo pa mu služi temeljno IP omrežje, ki zagotavlja varno povezavo med glavno kliniko in oddaljenimi lokacijami. Sistem bo nadomestil 8-delni ročni postopek s 4-delnim elektronskim, ki zajema tudi povezavo z laboratorijskimi rezultati. Na koncu bodo v kliniki dodali še aplikacijo, ki bo zdravnikom omogočila elektronsko predpisovanje zdravil in preverjanje medsebojnega delovanja zdravil. Klinika Suncoast bo dodala tudi sistem za vnos napotnic (physician order-entry — POE), ki bo ponudnikom storitev omogočal spletno naročilo diagnostičnih pregledov, kar pomeni, da zdravniki ne bodo več vezani na telefonske klice ali papirnate postopke. Adler je prepričan, da bodo prihranki do konca leta, ko bodo vse aplikacije v rabi, bistveni večji.
Adler pravi, da je uvedba EHR končni cilj za kliniko Suncoast. V njegovi viziji prihodnosti je celotna zdravstvena skupnost povezana s pomočjo učinkovite komunikacijske tehnologije.
"Menim, da bodo bolnišnice, kjer delujejo zdravniki v skupinah, še posebej, kadar so med sabo učinkovito povezani, imele bistveno prednost pred bolnišnicami, kjer ni tovrstne povezave," trdi Adler. "Vsi deli so med sabo povezani — bolnišnice in skupine zdravnikov — in lahko dostopajo do oddaljenih podatkov s pomočjo brezžične povezave. Tovrstne iniciative so bistvenega pomena za prihodnost."
Boljše povezave
Organizacija Foundation Surgery Affiliates se pripravlja na razvoj
Vodstvo organizacije Foundation Surgery Affiliates, ki ima sedež v Oklahomi in zajema dve kirurški bolnišnici in 18 kirurških ambulant (večina teh je v Teksasu), želi izboljšati zmožnost zagotavljanja najboljše možne oskrbe. Leta 2004 je bil njihov cilj povezano glasovno, video in
podatkovno omrežje.
jg 9
Slika 4 Digitalizacija je prisotna na vseh področjih.
Poleg tega je vodstvo želelo uvesti video konference in brezžično omrežje, da bi vzpodbudili višjo stopnjo sodelovanja in fleksibilnosti med svojim osebjem. Trenutno so največji tehnološki
Informatica Medica Slovenica 2005; 10(1)
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izzivi tega podjetja povezani z načrtovano širitvijo na dodatnih 50 lokacij v naslednjih petih letih.
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Slika 5 Sožitje sodobnih tehnologij.
"Za nas je bistvenega pomena, da so nam informacije na voljo, ko obravnavamo določen primer v kirurškem centru, saj lahko le tako izboljšamo kakovost storitev," pravi Mike Panas, direktor informatike. "To zajema zagotavljanje informacij o pacientu — kadar in kjer so potrebne — nevidno in v racionalnem časovnem okviru."
Panas verjame, da partnerji od ustanove pričakujejo, da bo zagotovila vrhunsko tehnološko platformo za kirurške centre in bolnišnice. Po Panasovih besedah se je ustanova odločila, da bo storila prav to.
"So zelo na tekočem z našim delom in ga dnevno spremljajo," razlaga Panas. "Postavljajo prava vprašanja, saj je bistven sestavni del storitev, ki jih ponujajo svojim bolnikom."
Foundation Surgery Affiliates je s tehnološko nadgradnjo začela maja 2003, ko so uvedli skupno omrežje z IP telefonijo in 100 telefoni. Postopoma je organizacija omrežje razširila še na kirurške centre. Mreža tako vključuje 500 IP in mobilnih IP telefonov. Septembra 2004 je organizacija začela prilagajati svoje omrežje, da bi ga kasneje lahko nadgradili z aplikacijami, ko bodo slednje na voljo. Te aplikacije vključujejo Cisco MeetingPlace, Advanced Cisco Device Management in Cisco IP/TV (ki prenaša visoko kakovostne video vsebine do notesnikov, učilnic in sejnih sob).
Organizacija je uvedla tudi brezžične povezave, ki 700 zdravnikom in partnerjem omogoča, da s pomočjo notesnika ali dlančnika dostopajo do zdravstvenih kartotek v Amarillu ali Houstonu. Zdaj lahko zdravniki z lahkoto dostopajo do zdravstvenih informacij v varnem okolju. Brezžična internetna povezava je zdaj na voljo tudi bolnikom, gostom in družinskim članom.
Nova rešitev videokonference omogoča zaposlenim, da na sestankih in konferencah komunicirajo na različne načine: od točke do točke ali od točke do več točk. Medicinska sestra lahko na primer razloži in predstavi določen postopek in tako opravi usposabljanje medicinskih sester v vseh prostorih organizacije. Panas pravi, da je organizacija s to rešitvijo bistveni zmanjšala stroške izobraževanja in potovanja.
"Zmanjšan čas potovanja nam omogoča, da ta čas izkoristimo za izobraževanje," razlaga Panas. "Investicija se nam bo samo s potovanji povrnila v 11 mesecih."
Povezano omrežje ima pozitivne učinke na ceno in izvajanje storitev. Ker so sistemi med sabo povezani, se lahko Panas hitreje odzove na potrebe bolnišnic in kirurških centrov.
"Prav zaradi tega imamo povezana omrežja," pravi Panas. "S pomočjo skladnih izdelkov in povezanih omrežij zmanjšamo odzivni čas. Za nas je najpomembnejši cilj, da še naprej učinkovito skrbimo za bolnike."
Izboljšana nega bolnikov
Bolnišnica Wilkes Regional nadgrajuje sistem za nove aplikacije
Kot številne druge bolnišnice je imela tudi bolnišnica Wilkes Regional Medical Center s sedežem v Severni Karolini zastarelo omrežno infrastrukturo. Odločili so se, da bodo nadgradili svojo informacijsko tehnologijo in s tem izboljšali varnost, mobilnost in učinkovitost, saj bi s tem
24
hkrati tudi izboljšali nego bolnikov in ostali konkurenčni. Tako bi zagotovili tudi ustrezno podlago za nove medicinske aplikacije, kot so mobilni vozički za medicinske sestre, EHR in sisteme za slikovne arhive in komunikacije (picture archival and communication system — PACS). Podjetje Internetwork Engineering, ki ima naziv Cisco Silver Certified Partner, je bolnišnici pomagalo preoblikovati in nadgraditi celotno IT infrastrukturo, kar je vključevalo uvajanje Cisco žičnega in brezžičnega omrežja ter Microsoftov sistem strežnikov Active Directory.
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Slika 6 Digitalizacija se začne že pri vnosu podatkov.
"Naš končni cilj je izboljšanje nege bolnikov s pomočjo omrežja, ki bo neprekinjeno aktivno," pravi Doug Brown, nadzornik v bolnišnici Wilkes Regional — ustanovi, ki združuje glavno bolnišnico s 130 posteljami, dislocirano ambulanto, welness center in oskrbo na domu. "Če je omrežje aktivno 99% časa, ne izgubite učinkovitosti v bolnišnici. Prav tako za njeno delovanje ne rabimo toliko virov in tudi tako privarčujemo."
Sistemi, ki so začeli delovati decembra 2004, podpirajo tudi potrebe po novih medicinskih aplikacijah — od mobilnih vozičkov za medicinske sestre do povezav na daljavo — za 70 zdravnikov.
Powers V: Zdrave povezave
Brezžično omrežje na primer zagotavlja varne povezave v celotni bolnišnici, kar sestram z 20 mobilnimi vozički omogoča, da dopolnijo klinično dokumentacijo. S pomočjo vozičkov sestre naredijo manj napak, preživijo več časa z bolniki ter imajo dostop do podatkov na mestu. Medicinske sestre niso več odvisne od spomina ali zapiskov.
Tovrstne aplikacije pa so šele začetek v bolnišnici Wilkes Regional. "Omrežje bi radi oblikovali tako, da bo podpiralo tudi aplikacije, ki jih bomo želeli dodati kasneje," pravi Brown. "Omrežje mora biti sposobno prenesti avdio in video aplikacije, pa tudi oddaljene povezave, po katerih je vse več zahtev."
Slika 7 IP telefon je računalnik v malem.
Na koncu bodo celotni zdravstveni informacijski sistemi stisnjeni v eno samo podatkovno bazo, kar bo poenostavilo iskanje podatkov iz različnih aplikacij. To bo zadnji korak v procesu uvajanja EHR v bolnišnici. V naslednjih 12 do 18 mesecih bodo zdravniki imeli dostop do podatkov v realnem času, lahko bodo vnašali spletna naročila
Informatica Medica Slovenica 2005; 10(1)
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in si ogledovali oddaljene zdravstvene kartoteke. S pomočjo teh sprememb naj bi bili bistveni podatki zdravnikom dostopni kadarkoli in kjerkoli.
"Vse več bolnišnic se zaveda potrebe po tehnologiji in dejstva, da se bodo morale razvijati naprej," trdi Brown.
IT oddelek v bolnišnici bo sedaj imel sredstva, da bo lahko že vnaprej reagiral na probleme in ne šele potem, ko se pojavijo. Tako bo osebje lahko več časa posvetilo širitvi, izboljšanju storitev in izobraževanju.
"Ključ do uspeha je delno tudi v individualnem izobraževanju zdravnikov, ki ga bodo po mojem mnenju dobro sprejeli," pravi Brown. "IT oddelku pa bo ostalo na voljo več časa za razvoj novih vsebin."
Naslednji koraki v zdravstvenem varstvu
Nenehna osredotočenost na oskrbo bolnikov
Organizacije, ki uspešno ustvarjajo omrežja zdravstvenega varstva, uporabljajo programske aplikacije in varna omrežja, kot pravi Mark Anderson iz skupine AC GROUP. Anderson meni, da je varno posredovanje informacij še vedno največji izziv. Ker vedno več klinik in bolnišnic posodablja svoje IT infrastrukture — in ker vedno več zdravnikov želi uporabljati tehnologijo za izboljšanje sodelovanja — se panoga zdravstvenega varstva razvija v trg učinkovitih rešitev, ki se osredotočajo na bolnike.
Poraba v zdravstvu v Združenih državah znaša 1,7 milijarde ameriških dolarjev letno, kar je 14 % bruto domačega proizvoda (BDP) — v večini ostalih razvitih držav znašajo stroški za zdravstvo le 8 do 10 % BDP. Aplikacije, ki delujejo v omrežju, npr. EHR in elektronski recepti, pomagajo preprečevati podvajanje zdravstvenih preiskav in preprečujejo drage
napake, ki nastanejo zaradi interakcije zdravil. Ne glede na to, kje se pojavljata — v omrežjih zdravstvenega varstva ali v notranjih sistemih ponudnikov zdravstvenih storitev — izboljšani dostop do informacij in združevanje sistemov zagotavljata številne priložnosti za varčevanje s časom zdravnikov, zmanjšanje bremena plačnikov in izboljšanje kakovosti oskrbe bolnikov.
Za več informacij o rešitvah podjetja Cisco Systems, pokličite na 01 5610 800 ali pa obiščite našo spletno stran
http://www.cisco.com/gov/iq/healthcare.
Zmanjšanje napak pri predpisovanju zdravil
Spletni dostop do seznamov odobrenih zdravil in podatkov o možni interakciji med zdravili lahko reši življenje. Mark Anderson, IT vizionar pri skupini AC Group, pravi, da so posledice napak pri predpisovanju zdravil lahko zelo hude, in ocenjujejo, da ameriška zdravstvena industrija porabi 10 do 15 milijard dolarjev letno za napake, ki so posledica medsebojne reakcije zdravil.
Razen zdravstvenega tveganja za pacienta "se stroški povečajo za 8000 USD vsakič, ko pride do napake," razlaga Anderson. "Ne gre za to, da zdravniki delajo napake, temveč za to, da nimajo dostopa do pravih podatkov ob pravem času. Le z avtomatizacijo lahko spremenimo zdravstvo in izboljšamo kakovost."
Anderson meni, da imajo le 3 do 4 odstotki zdravnikov dostop do potrebne tehnologije. Številni strokovnjaki definirajo potrebno tehnologijo kot navidezno "digitalno medicinsko ordinacijo prihodnosti", ki vključuje vodenje ordinacije, upravljanje dokumentov, EHR in IP telefonske sisteme z interaktivnimi spletnimi stranmi. Tako vsestranski sistem lahko zmanjša stroške in izboljša nego. Anderson pričakuje bistveno rast v povpraševanju po teh sistemih, pri čemer bo 30 % ponudnikov storitev povpraševalo že to leto. Trenutno ima določeno vrsto EHR-a 10% zdravnikov.
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Iljaž R: Elektronski zdravstveni zapis in "oniine" zdravstvene storitve v osnovnem zdravstvu
Strokovno-znanstveni prispevek ■
Elektronski zdravstveni zapis in "online" zdravstvene storitve v osnovnem zdravstvu
Rade Iljaž
Izvleček. Elektronski zdravstveni zapisi ali elektronski zapisi o bolnikih so se v osnovnem zdravstvu razvitih držav pojavili pred dobrima dvema desetletjema. Potrebe po njihovem razvoju so lahko precej različne gledano iz zornega kota bolnika oz uporabnika zdravstvene storitve, iz zornega kota zdravnika oz. ponudnika zdravstvene storitve in tudi z vidika države oz. lokalne skupnosti.Temeljno poslanstvo pa je predvsem podpora in pomoč pri celostni in vseživljenski zdravstveni oskrbi posameznika in skupnosti. V Sloveniji se bomo v naslednjih letih soočali s precejšnjimi izzivi pri spremljanju in izpolnjevanju zahtev iz akcijskega načrta EU za področje e-zdravja v Evropi.
Electronic Health Record and online health services in primary health care
Abstract. Electronic health records or electronic patient records in primary health care systems of developed world arose about two decades ago. The needs for their further development could be dramatically different depending on viewing angle. The most important are viewing angle of patient as user of health care services, the one of physicians', and finally the state's or community's angle of sight. The basic goal is professional support during the lifelong health care of individuals as well of whole community. Slovenia and its health care system has to face with many challenges of EU e-health action plan, in the following years,
■ Infor Med Slov: 2005; 10(1): 26-34
Institucija avtorja: Zdravstveni dom Brežice.
Kontaktna oseba: Rade Iljaž, ZD Brežice, Černelčeva 8, 8250 Brežice. email: rade.iljaz@guest.arnes.si.
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Uvod
Zanesljivo ne obstaja zdravnik, ki se večkrat med svojim delom ni začutil nemočnega pred goro podatkov, bodisi tistih ki bi jih moral vedeti o svojih bolnikih ali pa takšnih, katere s seboj prinaša čedalje hitrejši razvoj medicinske znanosti.
Tega dejstva se v zdravstvu, še posebej v t.i. primarnem zdravstvu, vsi prav gotovo dobro zavedamo. Mnogi smo že zdavnaj dojeli, da smo v spopadu s podatkovnim cunamijem, brez osnovnih znanj o uporabi računalniških "rešilnih čolnov", vnaprej izgubljeni. Implementacija informacijske tehnologije v naše ambulante zato že dolgo ni navadna modna muha, temveč temeljni pogoj za varno in uspešno zdravnikovo delo.
Konformizem in nezaupanje sta vendarle globoko zasidrana v človeški naravi, in so tako tudi v ZDA — računalniški zibelki in vodilni tehnološki velesili, računalniki v ambulantah izven bolnišnic, do pred letom ali dve bili bolj izjema kot pravilo.1
Hipoteza o nemoči pred informacijami bi z veliko verjetnostjo najbolje sodila k opisu zdravnika družinske oz. splošne medicine. Ti so pri svojem vsakdanjem delu dolžni upoštevati naslednja osnovna načela iz ustanovne listine o družinski medicini:2,3 splošnost, nepretrganost, usklajenost celovitost, sodelovanje, usmerjenost v družino in usmerjenost v skupnost.
Brez uporabe sodobnih informacijskih orodij in predvsem elektronske kartoteke je sledenje zapisanim načelom družinske medicine v sodobnih družbah praktično nemogoče.
Nastanek in razvoj elektronskih zdravstvenih zapisov
Prvič so se računalniki uporabili v ameriških bolnišnicah v začetku šestdesetih let prejšnjega stoletja in sicer v administrativno-finančne namene. V začetku sedemdesetih let so se pojavile
tudi prve različice elektronskih medicinskih zapisov (HELP - LDS Hospital inUtah; COSTAR — Massachusets General Hospital; TMR — Duke Hospital).4 Navkljub takratni smeli napovedi o hitri uvedbi računalnikov v vsakdanje klinično delo, veliki, dragi in počasni stroji niso ravno navdušili zdravstveno osebje. Na širšo rabo računalnikov v ambulantah izven bolnišnic je tako bilo treba počakati drugo polovico osemdesetih let.
Hiter razvoj digitalnih tehnologij in medicinske informatike je v zadnjem desetletju prejšnjega stoletja povzročil pravo informacijsko eksplozijo. Podvajanje hitrosti strojne opreme na vsakih 18 mesecev je spremljal precej počasnejši razvoj različnih programskih aplikacij, tudi tistih namenjenih zdravniškemu delu.
Devetdeseta leta so tudi v slovenskem prostoru zaznamovana s pospešeno uvedbo računalnikov v ambulante osnovnega zdravstva, ki so velikokrat obtičali na mizah medicinskih sester. Pridobili smo tudi "pametno zdravstveno kartico", kot nekakšen zametek osebnega zdravstvenega kartona. Na čedalje bolj dostopnem svetovnem spletu so se pojavili prvi medicinski i strokovni portali, razširila se je tudi uporaba e-pošte.
Ob začetku enaindvajsetega stoletja smo priča velikim vlaganjem in sistematičnem pristopu vseh razvitih držav pri implementaciji e-zdravja in elektronskih zdravstvenih zapisov.
Osnovna informacijsko-komunikacijska infrastruktura
Med medicinske zapise lahko prištejemo: vse rokopise in papirnate zapiske, računalniške zapise, zdravniške izvide in odpustna pisma, izvide laboratorijskih in drugih preiskav, rentgenske, UZ in vse ostale slikovne medicinske zapise, fotografije, zvokovne in videoposnetke ter različne izpiske medicinske opreme (EKG, EEG, EMG ipd.). Za vse našteto veljajo enaka etična in zakonska pravila o varni in zaupni rabi in shranjevanju.
28
Iljaž R: Elektronski zdravstveni zapis in "oniine" 
zdravstvene storitve v osnovnem zdravstvu
Ameriški nacionalni odbor za vitalno in zdravstveno statistiko je opredelil tri osnovne ravni v katerih lahko obravnavamo zdravstvene podatke.4 To so:
•	bolnikov zorni kot ali dimenzija osebnega zdravja,
•	dimenzija ponudnika zdravstvenih storitev,
•	dimenzija populacijskega zdravja ali zdravja v lokalni skupnosti.
Pogosto se isti zdravstveni podatki uporabljajo v različnih ravneh. Bolnik in zdravnik si tako delita bolnikove zdravstvene podatke ali npr. skupnost in zdravnik uporabljata iste registre, epidemiološke in statistične podatke.
Iz pet ključnih delov je zgrajena hrbtenica vsakega zdravstvenega informacij sko-komunikacijskega sistema. To so:5
•	Elektronsko medicinsko zapisovanje (EMR — electronic medicial record). Elektronski medicinski zapis predstavlja shranjene podatke o bolnikih, na lokalnem sistemu v za to posebej prirejenem programu.5,6
•	Elektronski zdravstveni zapisi / kartoteke (EHR — electronic health record). Po opredelitvi Evropske komisije so to:7 "digitalno shranjene klinične in administrativne zdravstvene informacije o vseh prejšnjih zdravstvenih težavah za potrebe zdravstvene oskrbe, poučevanja in raziskovanja ob zagotovljeni zaupnosti podatkov. Elektronski zdravstveni karton naj bi bil orodje za pomoč pri nudenju zdravstvene oskrbe na vseh nivojih in segmentih oskrbe, ki je dostopno preko zdravstvenih računalniških omrežij".
•	Osebni zdravstveni zapisi (PHR — personal health records). Predstavljajo osebne elektronske zbirke podatkov o lastnem
zdravju z možnostjo selektivnega dostopa do podatkov.
•	Standardi. V svojem najširšem pomenu označujejo terminološke standarde in standarde za zapisovanje, hranjenje in izmenjavo podatkov.
•	Oprema za izmenjavo podatkov so servisi za izmenjavo podatkov med različnimi izvajalci in sistemi.
Elektronski zdravstveni zapis in njegove razsežnosti
S precejšnjo terminološko zmedo, glede opredelitve elektronskega medicinskega zapisa smo se v strokovni literaturi lahko srečali še do pred nekaj leti. Izmed številnih pojmovanj in okrajšav
le-teh (PMRI, CMR, CPR, PCR, DMR, EPR, CCR), se v ameriški literaturi danes srečamo predvsem s tremi prej omenjenimi (EMR, EHR in PHR).5 V Združenem kraljestvu je v ospredju vendarle izraz "elektronski zapis o bolniku" (electronic patient record — EPR).8
Poleg že omenjene opredelitve Evropske komisije,7 velja omeniti pragmatično angleško pojmovanje elektronskega zapisa,9 da je to "longitudinalno zapisovanje podatkov o zdravju in zdravstveni oskrbi posameznika, ki združuje podatke iz osnovnega zdravstva s podatki o oskrbi in občasnih pregledih pri drugih izvajalcih zdravstvenih storitev". Še najbolj jedrnata je slovenska različica,5 ki elektronski zdravstveni zapis opredeljuje kot: "širšo množico podatkov, oz. združenih informacij iz različnih virov, ki se nanašajo na eno osebo". V Slovenskem medicinskem slovarju10 opredelitev izraza "zdravstveni karton" zajema širše pojmovanje od izraza "zapis", vendar se je v vsakdanji strokovni rabi uveljavil izraz elektronski zdravstveni zapis.
Sodobni elektronski zdravstveni zapisi bi naj imeli vsaj 10 razsežnosti in sicer: zajem informacij, prikaz informacij, vsebinski del, upravljanje s podatki, nadzor
Informatica Medica Slovenica 2005; 10(1)
29
in preverjanje sistema, standardi kakovosti, varnost in zaupnost, dobra klinična praksa, računalniško podprto odločanje in nenazadnje interoperabilnost.11
Temeljno poslanstvo elektronskih zapisov je predvsem podpora in pomoč pri celostni in vseživljenski zdravstveni oskrbi posameznika in skupnosti.
IOM je leta 2003 poudaril osem osnovnih lastnosti vseh elektronskih programov za zapisovanje
podatkov:12
1.	Dostopnost podatkov. Mora biti zagotovljen takojšen dostop do vseh podatkov, ki so lahko zdravniku v pomoč za pravočasno in pravilno odločanje.
2.	Rezultati preiskav. Dostop do ugotovitev prejšnjih preiskav in pregledov
3.	Urejanje podatkov. Možnost računalniškega vnosa in shranjevanja
Slika 1
Danes v številnih državah lahko zasledimo množice elektronskih zdravstvenih zapisov, ki se po svojem namenu in zgradbi med seboj precej razlikujejo. Tako nekateri zajemajo skoraj vse
podatkov o vseh zdravilih, testih in drugih zdravstvenih uslugah.
4.	Podpora pri odločanju.
5.	Elektronske povezave in komuniciranje.
6.	Podpora bolnikom. Zagotavljanje dostopa bolnikom, do njihovih medicinskih kartotek, interaktivno vzgojo in možnost nadzora na domu in samotestiranja.
7.	Administrativne naloge. Orodja, vključno z moduli za naročanje, ki lahko izboljšajo administrativne naloge in oskrbo bolnika
8.	Sporočanje. Shranjevanje elektronskih podatkov, po enotnih standardih, ki omogočajo zdravnikom, in zdravstvenim organizacijam pravočasno sporočanje skladno zahtevam zveznih in zasebnih služb.
	Prevzem IT	
		
	i	r
Izkoriščanje IT		
omenjene lastnosti in razsežnosti, drugi pa zgolj služijo za izpis zdravil ali shranjevanje kliničnih in laboratorijskih izvidov.1, 12 V grobem, lahko opredelimo osnovne in sekundarne namene
Prilagojeni algoritem privzema informacijske tehnologije (itam) po dixonu.
30
Iljaž R: Elektronski zdravstveni zapis in "oniine" zdravstvene storitve v osnovnem zdravstvu
elektronskih zapisov. Med osnovne namene lahko razvrstimo: nudenje in vodenje zdravstvene oskrbe in ambulante, podpora zdravniku pri odločanju, administrativno-finančni vidik in podpora bolniku pri nadzoru njegove bolezni. Nekateri pomembnejših sekundarnih namenov elektronskih zdravstvenih kartotek so: izobraževalni, raziskovalni, regulativni vidik, zdravje in varnost skupnosti.12
Tabela 1 Anketa 93 zdravnikov družinske medicine na Gorenjskem, Košir 2005.
Gorenjski zdravniki, služba
Računalnik
E-mail Word, Excel Invalidske Delno kartoteka Cela kartoteka
58% 13% 28% 31% 12% 0%
referenčnih modelov, postopkov in standardov ter finančne spodbude države.15
>
o £
C •Q
2 o
Q. 3
O ■>
0)
25 20 15
Int
povezava od doma
Int
povezava -služba
							□	Analog □	IDSN □	ADSL □	Kabel □	Nič □	Ne vem
							
							
	f		r				
		r			□		
Slika 2 Dostop do svetovnega spleta med zdravniki Dolenjske, Posavja in Ljubljane; Iljaž in Kersnik, 2004.
0
Prevzem in uporaba informacijske tehnologije (IT)
Ameriški informatik Davis je že leta 1985 razvil model sprejemljivosti tehnologije (TAM), da bi pojasnil vedenje uporabnika računalnika.13 Po tem modelu, posameznik bo želel narediti to kar referent X misli, da ona ali on morata storiti, predvsem zaradi ujemanja z njenimi/ njegovimi lastnimi stališči in ne zaradi vpliva referenta X.
Prvotni model sta kanadska družinska zdravnika Dixon in Stewart prilagodila za potrebe raziskovanja privzema informacijske tehnologije med kanadskimi zdravniki družinske medicine — slika 1.14
Na nacionalni ravni so priporočila in strategije za uspešno implementacijo informacijske tehnologije in elektronskih zdravstvenih zapisov v vsakdanjo prakso lahko precej različna. Kot glavne razloge za visoke odstotke informatiziranosti zdravniških delovnih mest v osnovnem zdravstvu nekaterih evropskih držav se omenja: izdelane akcijske načrte, organizirano in načrtovano usposabljanje in izobraževanje zdravnikov, vodilno vlogo poklicnih zdravniških združenj, izdelavo
Informacijska tehnologija v slovenskem osnovnem zdravstvu
Raziskave o uporabi informacijske tehnologije v slovenskem osnovnem zdravstvu so redke, zato je tudi težko objektivno oceniti resnično stopnjo rabe in koristnosti obstoječe programske podpore za delo s elektronskim zapisom o bolniku.
Nekaj manjših študij so nakazale nizko stopnjo uporabe tovrstnih programov16,17 kar se tudi ujema z neuradnimi podatki (strokovni nadzori, ogledi ambulant, dostop do interneta). Še najbolj zaskrbljujoče je pomanjkanje jasnih ciljev in strategij. Za začetek bi verjetno zadostovalo zgledovanje po nekaj uspešnih tujih modelih, najprej tistih v državah s severa Evrope (Skandinavija, Anglija, Nizozemska, Irska).
Tabela 1 povzema nekaj rezultatov iz raziskav o uporabi računalnikov in elektronskih medicinskih kartotek med zdravniki na Gorenjskem.
Na sliki 2 je prikazana uporaba in oblika dostopa do svetovanega spleta, med zdravniki Dolenjske, Ljubljane in Posavja.
Informatica Medica Slovenica 2005; 10(1)
31
Študija iz leta 2004, med zdravniki Ljubljane, Dolenjske in Posavja je med drugim pokazala, da se od elektronskih zapisov o bolniku predvsem pričakuje preglednejše delo in podpora pri kliničnem odločanju. Hkrati so sodelujoči v študiji, tako ocenili možne ovire za pospešeno informatizacijo (Lestvica po Likertu od 1 do 5):
•	Pomanjkanje časa — 3,625 (IZ: 0,64)
•	Pomanjkanje posluha pri zdravstvenem managementu - 3,43 (IZ: 0,47)
•	Omejitve obstoječe programske opreme —
3,37 (IZ: 0,396)
•	Stroški nabave in vzdrževanja — 3,21 (IZ: 0,45)
•	Usposobljenost zdravstvenega osebja —
3,05 (IZ: 0,36)
•	Varnost podatkov — 2,95 (IZ: 0,43)
Elektronsko mreženje
Kot je že uvodoma omenjeno, nepretrgana in usklajena skrb za zdravje posameznika in lokalne skupnosti (continuity of care) je bistvena, če ne najpomembnejša sestavina dela zdravnika v osnovnem zdravstvu (primary health physician). Podatki o prejšnjih bolnikovih težavah, zdravljenih in sedanji terapiji so zanj nepogrešljivi pri nudenju kakovostne zdravstvene oskrbe. Ta vidik zdravstvene oskrbe je poudarjen tudi v različnih smernicah za dobro prakso pri rabi elektronskih zapisov o bolnikih.2,18,19
Splošno priporočilo je, da bi vsi zdravniki znotraj lokalne skupnosti (mreže) imeli dostop do pomembnih podatkov o bolniku, razen takrat, ko bolnik to izrecno prepove. Elektronska mreža bi morala biti lokalna in jasno opredeljena.
Vsaki bolnik bi moral vnaprej biti seznanjen z vsemi možnostmi dostopa do njenih/njegovih
zdravstvenih zapisov. Glede vprašanj o zaupnosti in varovanju bolnikovih podatkov je torej v ospredju princip bolnikovega "obveščenega pristanka" (informed consent).20
Primer iz prakse
ZD ima utečeno informacijsko mrežo, s povezanimi delovišči, tako je zdravnikom omogočen dostop tudi do podatkov bolnikov iz drugih ambulant (ob prej omenjenih pogojih). Ob odhodu nekaterih zdravnikov v samostojno prakso, ni več možen dostop do podatkov teh ambulant ob pregledu njihovih bolnikov. Poveča se število napotitev in predpisanih zdravil, tudi možnost zdravstvenih napak.
Vprašanje za razpravo: "Kakšne so možnosti za ureditev tovrstnih vprašanj s strani zakonodajalca in zdravstvenih zavarovalnic?"
V slovenskem prostoru smo že pred leti beležili uspešne poskuse elektronske izmenjave podatkov med primarno in sekundarno ravnijo v okviru regijskih elektronskih omrežij.21 Žal, od takrat na področju graditve uspešnih lokalnih elektronskih omrežij med različnimi izvajalci zdravstvene dejavnosti, ni bilo bistvenih premikov.
"Online" zdravstvene storitve
Redko kdo še podvomi v močen vpliv, ki ga imata uporaba interneta in elektronske pošte na zdravstveno oskrbo v osnovnem zdravstvu, podobno kot je širša raba telefona dramatično spremenila zdravniško prakso. Ob vseh možnostih elektronske komunikacije na svetovnem spletu naše sporazumevanje z bolnikom lahko oplemenitimo z novo dimenzijo. Posvet z bolnikom je po tej poti učinkovit in po ceni, tudi ko sta z njegovim zdravnikom tisoče kilometrov narazen.
Vendarle so v nekaterih študijah o uporabi e-medicine tudi ugotavljali, da so poskusi uvajanja informacijske tehnologije v zdravniško prakso
32
Iljaž R: Elektronski zdravstveni zapis in "oniine" zdravstvene storitve v osnovnem zdravstvu
prepogosto podcenjevali različne socialno-demografske vplive.22
Nekaj tovrstnih storitev je neposredno povezano s funkcionalnostjo elektronskega zapisa, kot npr.: sporočanje izvidov, dostop do lastne kartoteke, naročanje receptov, nega in zdravljenje na daljavo (telemedicina).
Druge "online" storitve so neodvisne od uporabe elektronskih zapisov, npr.: e-zdravstveno svetovanje, naročanje in nakup zdravil v pooblaščenih lekarnah, naročanje na preglede, operacije ipd.
Iskanje podatkov povezanih z zdravjem in zdravstveno oskrbo je danes že tretji najpogostejši razlog uporabe svetovnega spleta, takoj za spletnim nakupovanjem in uporabo elektronske pošte.23,24
Pri vseh oblikah e-zdravljenja, svetovanja ali nudenja spletnih storitev se je potrebno tudi zavedati različni strokovnih pasti in etičnih pomislekov, predvsem pa zakonskih določb o zaupnosti, verodostojnosti, celovitosti in varovanju osebnih podatkov.25,26
Pri tem so pričakovanja bolnikov oz. uporabnikov tovrstnih storitev različna in dokaj visoka glede dostopnosti in čim krajših odzivnih časov ponudnika storitve.27,28
Izzivi bližnje prihodnosti
V slovenskem prostoru bo v naslednjih letih, na področju e-zdravja potrebno narediti nadaljnje korake in čim hitreje ujeti priključek z vodilnimi državami v EU.
Osnova za uspeh kakršnekoli strategije je večanje funkcionalnosti in izpopolnjevanje obstoječih elektronskih zapisov hkrati s spodbujanjem k čim širši uporabi računalniške tehnologije med zdravniki osnovnega zdravstva. Predvsem bo dosti več potrebno narediti pri določanju standardov, upoštevanju pripomb in potreb uporabnika programske opreme ter stalni evaluaciji obstoječih
programov. Majhnost slovenskega trga, administrativna naravnanost sedanjega sloga dela zdravnikov družinske medicine, pasivna vloga države in strokovnih združenj, so le nekatere izmed možnih ovir na trnovi poti uspešnejšega prevzema akcijskega načrta EU. Ta je pa svojih časovnih okvirih nedvomen, ko pravi: "Do leta 2005 mora vsaka od držav članic izdelati nacionalni in regionalni zemljevid za potrebe e-zdravja. Morali bi se osredotočiti na razvoj e-zdravstvenega sistema, določiti cilje za zagotovitev notranje operativnosti in uporabo elektronskega zdravstvenega kartona".29 Naprej načrtu EU pravi, da "Države članice naj v sodelovanju z Evropsko Komisijo do konca leta 2006 opredelijo in izdelajo načrt za standarde za zdravstvene podatke ter elektronski zdravstveni karton, ki bodo omogočili medsebojno povezavo različnih zdravstveno informacijskih sistemov v Evropi. Pri tem naj upoštevajo dosedanje izkušnje ter dosežke na področju standardizacije".
Med drugim, načrt EU predvideva, da bi se do leta 2010 5% vseh sredstev za zdravstvo namenjalo v e-zdravje.29
Zaključek
Sedanja stopnja rabe elektronskih zdravstvenih zapisov je med zdravniki slovenskega osnovnega zdravstva, primerjalno z večino držav v EU, zaskrbljujoče nizka. Implementacija in posodabljanje tovrstnih programov se izvaja brez jasnih standardov in brez sodelovanja poklicnih združenj. Razvoj obstoječih elektronskih zapisov o bolniku je, še vedno prednostno usmerjen v izpolnjevanje administrativno-finančnih nalog zdravstvenih zavarovalnic in manj v zagotavljanje kakovostnejše zdravstvene oskrbe. Računalniško podprto strokovno odločanje zaenkrat ne obstaja niti v najbolj enostavni obliki.
Ni tudi izdelanih načrtov za evaluacijo programske opreme in preverjanje zadovoljstva končnih uporabnikov z le-to.
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Slaba motiviranost zdravnikov za prevzem računalniške tehnologije je glede na vse omenjeno in možne visoke stroške nabave in vzdrževanja nove tehnologije, povsem razumljiva.
Izpolnjevanje nalog iz akcijskega načrta EU za področje e-zdravja, bo v sedanjih razmerah skrajnje težka naloga.
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17.	Košir B. Uporaba računalnikov pri ambulantnem delu. II srečanje mladih zdravnikov družinske medicine, Ljubljana 2005.
18.	Middleton B, Hammong WE, Brenan PF, Gregory FC. Accelerating U.S. EHR Adoption: How to Get There From Here. Recommendations Based on the
2004 ACMI Retreat. JAMIA 2005; 12/1: 13-19.
19.	Electronic Decision Support for Australia's Health Sector. Report to Health Ministers by the National Electronic Decision Support Taskforce, Canbera
2003.
20.	Braddock HC. Informed Consent. In: Sugarman J. 20 common problems - Ethics in Primary Care, Mc Graw Hill 2000: 239- 54.
21.	Kersnik J. Berčič B, Rems M. Elektronska izmenjava medicinskih podatkov med primarno in sekundarno ravnijo v okviru elektronskega regijskega omrežja. Zdrav Vestn 1999; 68: 503 -5.
22.	P. Itkonen. Development of a regional health care network and the effect of knowledge intensive work on personnel and organization. Methods
Inform. Med. 2002; 41: 387-92.
23.	Bernstam EV, Shelton DM, Walji M, Meric-Bernstam F. Instruments to assess the quality of health information on the World Wide Web: what can our patients actually use? International Journal of Medical Informatics 2005; 74:13-19.
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24.	Fox S, Fallows D. Internet health resoruces. Peew Internet and American Life Project, Washington
DC, 2003.
25.	Car J, Sheikh A. Email consultations in health care: 1—scope and effectiveness
BMJ, 2004; 329: 435 - 438.
26.	Car J, Sheikh A. Email consultations in health care: 2—acceptability and safe application BMJ,
2004; 329: 439 - 442.
27.	P.S. Whitten, Mair F. Telemedicine and patient satisfaction: current status and future directions.
Tlemed. J E-health 2000; 6:417-23.
28.	Couchman GR. Forjuooh SN, Rascoe TG, Reis MD, Koehler B, Walsum KL. E-mail communications in primary care: what are patients' expectations for specific test results? International Journal of Medical informatics, 2005;
74: 21-30.
29.	Eržen I. e-Zdravje za boljšo zdravstveno oskrbo prebivalcev Evrope: Akcijski načrt na področju e-zdravja v Evropi - neuradni prevod dokumenta Evropske Komisije, 2005.
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Research paper ■
Molecular Imaging by Proton Magnetic Resonance Imaging (MRI) and MR Spectroscopic Imaging (MRSI) in Neurodegeneration
Rakesh Sharma
Abstract. Proton magnetic resonance imaging (MRI) and proton magnetic resonance spectroscopic imaging (MRSI) as integrated method was used to investigate NAA, creatine and choline changes in brain metabolites in Alzheimer's disease and epilepsy. Multislice H-1 MRSI technique of human brain, without volume preselection was used. Furthermore, MRI segmentation and completely automated spectral curve fitting facilitated quantitative data analysis in Alzheimer's disease and molecular imaging basis of prediction for epilepsy at different locations in the brain. MRI and MRSI integrated method was evaluated to characterize the anatomical and metabolite changes in disease.
■ Infor Med Slov: 2005; 10(1): 35-55
Author's institution: Magnetic Resonance Spectroscopy center, DVA Medical Center, San Francisco.
Contact person: Rakesh Sharma, Magnetic Resonance Spectroscopy center, DVA Medical Center, 4150, Clement Street, San Francisco, CA 94121. email: rs2010@Columbia.edu.
36
Sharma R: Molecular Imaging in Neurodegeneration
Introduction
MRI segmentation and co-analysis with H-1 MRSI appears to predict spectral peak variation due to brain tissue composition enclosed in a MRSI voxel which can mimic changes of the metabolite peak intensities. So, quantitative analysis of H-1 MRSI data should be able to distinguish the pixel intensity changes due to changes of the metabolite concentration from the intensity changes due to artifacts of partial volume effects. However, decreased NAA along with elevated Cho may be useful in differentiating axonal losses due to myelin breakdown from other segmented MR images.1 The analysis includes point spread function and chemical shift displacement effects. Earlier, contributions to the observed NAA resonance from gray matter (GM) and white matter (WM) after nulling cerebrospinal fluid (CSF) or regions outside the brain was reported for application to brain tissue analysis.2 The tissue content p=(GM+WM) and an index for the gray matter f=GM/(GM+WM). The NAA intensity correction for tissue volume was reported as NAAcorrected = NAA/p. The index f is used in statistical data analysis to account for variations of GM/WM voxel composition. Proton MRSI measures the regional distribution of important cerebral metabolites at much coarser spatial resolution than MRI due to sensitivity reasons. Mainly three amino acid metabolites are MRS visible from N-acetyl aspartate (NAA), choline (Cho) and creatine (Cr). NAA is found in neurons exclusively hence remains undetectable in other cell types. The Cho resonances in H-1 MR brain spectra originate predominantly from choline itself and from glycerophoshocholine and phosphocholine. These compounds are constituents of membrane phospholipids metabolism. Cho is known to increase markedly after myelin erosion.3 Hence, choline is suspected to axonal losses not associative with myelin breakdown. Creatine resonance represents the sum of creatine and phosphocreatine that are both present in neural and glial cells.
H-1 MRSI using single volume pre-selection localization by PRESS method is significant in predicting epilepsy sensitive regions such as mesial temporal lobe, right and left hippocampus. In this paper, application of proton MRSI and MRI techniques is described to study neurodegenerative diseases such as Alzheimer's disease (AD), possibility of better classification of epilepsy using MR spectroscopic imaging such as mesial lobe epilepsy (MLE) and temporal lobe epilepsy (TLE). In AD, MRI showed a general loss of cortical tissue and volume reduction of hippocampus, a region involved in predictable memory functioning. Similarly, MRI studies showed tissue losses in the region of seizure focus i.e. in the hippocampus or in the neocortex of TLE patients. Although these anatomical changes are useful in diagnosis, they are not conclusive.
Principles of MRSI and metabolite imaging:
Spatially selective RF excitation pulses in 3 orthogonal directions are applied on brain to get transient NMR time-domain signal which is further transformed into frequency domain signal intensity by Fourier transform of the acquired data. These NMR resonance frequencies of different contents, f (in Hertz), is defined by
f = y(Bo Belectron)= -^.(1-0),	Eq. 1
where the term o is electron shielding effect or chemical shift of molecules oriented in Bo field. However, in vivo imaging only scalar component of motional averaging effect is measured. Based on the chemical —CH3, —CH2 or —CH= groups, the spectral chemical shift peaks are generated and chemical shift frequencies generate different metabolite images such as NAA, Cr and Cho images. These images also depend on Bo field strength, spin-echo time (TE), field strength resonant frequency due to sum of chemical shift and spin-spin interaction or J-coupling (hertz). However, long TE and transverse relaxation T2 reduce complex and overlapping spectrum peaks of different insignificant metabolites.
Water resonance is used as internal standard based on using series of Rf pulses and gradient
Informatica Medica Slovenica 2005; 10(1)
37
pulses to achieve water magnetization spread over all phase angles in transverse plane. If water is suppressed, 1H spectrum and images are dominated by three singlet resonances from the protons (—CH3 groups) in NAA at 2.0 ppm, Cr at 3.0 ppm, PCr at 3.03 ppm and Cho at 3.2 ppm having long T2 values and visible at longer TE. These metabolites and other metabolites such as y-aminobutyric acid, glutamine, myo-inositol, taurine, amino acids have been reported for neurodegeneration and demyelination.4 Short TE 1-H spectra for proteins and short relaxing large weight molecules such as lipids have been visualized in multiple sclerosis, necrosis.5
Data acquisition in MRSI: 3D data set is acquired by slice excitation for metabolite resonances which are integrated over brain regions for all voxels to give co-registered image. Image dependence on signal amplitude, frequency shift, inhomogeneity, phase may be described as
S(kx,ky,t)=//!p(x,y,r).
e—i(œ(r)+yABo(x,y))t +
xy
kxy + 0(r) + 0(x, y)) -
Eq.2
t/T2(r).3x,3y,3r
where index r is resonance(s), p(x, y, r) is signal amplitude distribution for each resonance, ABo (x,y) is a spatially dependent frequency shift due to magnetic shift inhomogeneity, y gyromagnetic ratio, 0(r) is phase of each resonance, 0(x,y) is zero-order phase term, T2(r) is the transverse relaxation rate of each resonance which is spatially variant, kx, ky are spatial encoding terms. Commonly used gradient phase encoding in all spatial dimensions, predict kx as 3D variant depends on encoding gradient Gx applied
in time te,
Kx = y Jenc oGx(t).t. 9t
Eq.3
Images of NAA, Cr and Cho metabolite distribution can be generated by integrating over corresponding spectral regions for all points in spatial dimensions. Time deconvolution corrects time-varying values of ABo(x,y) due to gradient eddy currents.
Co-analysis of MRI and MRSI: It is difficult to get 100% pure MR signal from one tissue due to mixed tissue distribution, partial volume contribution. It effects metabolite quantitation. Coanalysis of MRSI data from co-registered high resolution tissue segmented MRI data minimizes mixed signal effects such as CSF nulling and tissue fractional analysis by segmentation techniques.5 However, Rf coil sensitivity distribution and volume selection profiles contribute to the signal from each voxel. By convolution of MRSI SRF with tissue distribution function and color coding 2D data of slice selection profile as a function of chemical shift offset of each metabolite may be described. For tissue, t, contributing to a voxel at position (x,y) for metabolite, m, in a 2D MRSI data set at slice position, z, the effective volume may be described as
Vt.m(x,y,z)=IIt(x',y',z')SRF(x-x',y-y').Pm(z-z')Cm(x',y',z')
Eq.4
where It is the MRI-resolution distribution function for tissue type t; Pm is the slice-selective profile, which will vary as a function of the chemical shift of the selected metabolite resonance; and Cm represents additional factors of intensity distribution, Rf coil sensitivity and volume selection methods. Indices x' and y' cover the SRF within the image plane for the voxel (x,y) and z' must span all MRI slices that cover the MRSI slice selection profile. However, we used this analysis for separate signal contributions from white matter and gray matter. The signal of each metabolite can be described as
Sm(x,y,z) =SVt,m(x,y,z)pt,
Eq.5
where ptm is signal for metabolite m and tissue type t which has been assumed regionally invariant over the selected voxels. The distribution of gray matter and white matter and CSF may be analyzed by multiparametric linear regression and further extrapolation to calculate 100% of each tissue and metabolite signal ptm. Finally, co-registration of this data to nonlinear spatial transform into coordinate system. In present report, coanalysis of metabolite levels with volume measurements of
38
Sharma R: Molecular Imaging in Neurodegeneration
brain regions obtained from MRI included hippocampal volume for lateralization of epilepsy and ventricular or hippocampal volume reduction in Alzheimer's disease are reported. However, the analysis suffers from inaccuracy due to spatial variations of tissue distribution even in normal brain.
Methods and Materials
AD Patients and control subjects: Twelve patients (mean age ± s.d. 74.3 ± 8.0 years), ranged from 55 to 82 years old (8 females, 4 males) with the diagnosis of AD (9 probable, 3 possible AD) and 17 cognitively normal subjects of similar age and sex distribution were studied. The subjects were recruited from the different Alzheimer local centers, were examined by a neurologist, and had the standard battery of blood and neuropsychological test at the centers. The 17 control subjects had an evaluation similar to that of the AD patients and were judged to be normal cognition and function. None of the patients or controls had evidence of stroke, cortical or subcortical infarctions, or other major abnormalities on MRI scan which a neurologist reads. The combined MRI/MRSI protocol was completed by all control subjects and by 10 AD patients, while two patients did not complete MRI scan.
Mild Cognitive Impairment (MCI): Seven subjects (mean age ± s.d.; 72.8 ± 10 years range 56 to 85, 4 females, 3 males) who were not demented according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria, presenting mild impairments of memory and/or cognitive functions, and having a Clinical Demetia Rating Score of 0.5 were selected for this study. These data were compared to 7 age matched cognitively normal subjects with a similar sex distribution (5 females, 2 males). The MCI subjects were recruited from the Alzheimer's centers, were examined by a neurologist, and had the standard battery of blood and neuropsychological tests at centers. The control 7
subjects had an evaluation similar to that of the MCI subjects and were judged to be normal cognition and function. No subject had evidence of stroke, cortical or subcortical infarctions, or other major abnormalities on MRI scans which were read by a neuropsychologist.
Diagnostic MRI examination: All studies were performed on a 1.5 Tesla Magnetom VISION system (Siemens Inc. Iselin, NJ) equipped with standard quadruple head coil. To minimize motion of the patient head, a vacuum-molded head holder (VacPac, Olympic Medical, Seattle WA) was employed to restrict head movements. The MRI protocol consisted of sagittal T1 weighted localizer scans, oblique axial double spin echo (DSE) scans angulated parallel to the optic nerve as seen in the sagittal plane, and a volumetric (3D) magnetization prepared for rapid gradient echo (MP-RAGE) acquisition angulated perpendicular to the DSE image planes. These examinations yielded T1-weighted coronal images estimated to be orthogonal to the long axis of the hippocampus. The measurement parameters of DSE were: TR/TE1/TE2 = 300/20/80 ms, 1.0 x 1.4 mm2 resolution, and 48 to 51 contiguous 3 mm thick slices, covering the entire brain from the inferior cerebellum to the vertex. The measurement parameters of 3D MP-RAGE were TR/TI/TE=10/250/4 ms, flip angle 15,1.0 x 1.0
mm2 resolution, and 1.4 mm thick partitions. MRSI acquisition methods
All magnetic resonance studies were performed on a whole body 1.5 T Magnetom and magnetic resonance spectroscopic system. Multisection Spectroscopic Imaging (MSSI) and Point-resolved Spectroscopy (PRESS) were used. CHESS pulses were used in MSSI sequence. For MRI, magnetic resonance slices were angulated along the canthomeatal AC-PC line. 20 contiguous sections of 5 mm slice thickness and 0.5 mm section gap, TR=3000 ms, TE=30 and 80 ms were obtained to cover the entire brain from cerebellum to vertex. The magnetic resonance images were qualitatively evaluated without knowledge of subject's
Informatica Medica Slovenica 2005; 10(1)
39
diagnosis. Ventricular dilatation and sulcus widening were rated as mild, moderate and severe. Similarly, white matter signal hyperintensity (WMSH) were observed on the tip of lateral ventricles or periventricular rims or subependymal or subcortical regions. After MRI, acquisition of MRSI 17 mm-thick volume of interest (VOI) corresponding in location and thickness to the three MRI slices superior to lateral ventricles were selected for H-1 MRSI. The anterior-posterior and left-right dimensions of the H-1 MRSI volume of interest were adjusted according to the brain size. The position of a typical VOI 180 mm2, phase-encoding 16 x 16, in-plane resolution 11 mm, spectroscopic volume 2.2 ml were obtained in 34 minutes.
Magnetic resonance imaging segmentation and hippocampal volume measurements
Magnetic segmentation and voluming: Tissue segmentation on the whole brain and voluming of the hippocampus was performed using software Spectroscopic Imaging Display (SID), developed in home laboratory. The semi-automated
segmentation software was used for both 3D T1-weighted MP-RAGE and T2-weighted spin echo images. The first pass segmentation procedure automatically removes the skull and meninges from the images as shown in Figure 1. It co-registers the 3D T1 weighted images with each of the two interleaved spin-echo images.6 It performs 3D inhomogeneity correction using a digital filter and performs the segmentation on the whole brain using K-means cluster analysis via the SAFASTCLUS procedure.7 For this cluster analysis, seeds for each tissue category, i.e. gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), were first defined based on the regions around the peaks in the T1 pixel intensity histogram. These regions represented the conservative estimates of the approximate tissue category.8 The initial process was followed by mutual editing of the data to separate cortical pixels from subcortical GM, ventricular CSF from sulcal CSF. This process reclassified those pixels incorrectly classified as GM into a category of white matter signal hyperintensity. The number of pixels for each tissue category was expressed as a percentage of total intracranial volume (TIV = total number of pixels).
.'? w I
i - ■.	4
t r

A	B
MC ^ ■ F1 sc : • ~ "<	Ar/^- Af> * rh^y
Figure 1 A localizer sagittal image (left on top), location of VOI (second on left) and different brain regions (left on bottom), corresponding spectrum at left and right locations (panels in center) and spectral peaks from 8 locations (panels on right) are shown to analysis tissue composition and metabolite concentrations in AD.
Quantitative estimates of the volumes of right and left hippocampus were obtained by using coronal T1-weighted MP-RAGE images. These images resliced the whole hippocampus perpendicular to
the long axis of the hippocampus. Boundaries of the hippocampus were drawn.6 The area of hippocampus in all regions of interest was automatically calculated and multiplying that
40
Sharma R: Molecular Imaging in Neurodegeneration
value by the slice thickness derived the total volume. The operator who was blinded to the patient's diagnosis performed all volume measurements. To adjust for inter-individual variations in head size, each subject's hippocampal volume (HV) was normalized to his or her intracranial volume index (TIV/TVI), which was the ratio of mean Total Intracranial Volume (TIV) in the control group and Total Intracranial Volume (TIV) in patient group, computed from the segmented images.9 This procedure maintained the dimension of the normalized hippocampal volumes in contrast to the normalization calculated by 1/TIV. It was justified as long as the mean TIV values of the study groups are not significantly different. In this population, patients and control subjects had a comparable TIV values (difference < 1%, p < 0.96 by ANOVA), suggestive of modest correlation between reduced TIV sizes and deficient cognitive functions.
MRSI postprocessing: After acquisition, the H-1 MRSI data were zero-filled to a rectangular matrix of 32 x 32 x 1024 points, Fourier transformed, and phase and baseline corrected using software developed in home laboratory. Four Hz Gaussian line broadening was used in the spectral direction, and mild Gaussian apodization was applied along the spatial direction to reduce Gibbs ringing effects, resulting in an effective volume of the MRSI voxels of approximately 1.6 ml. Voxels were selected from head, body, tail of the right and left hippocampus and the resonances from NAA, Cr and Cho were curve-fitted using NMR1 software (New Research Methods Inc. Syracuse, NY). In order to estimate the absolute concentrations in mmol/L, integral values of the metabolite resonances were referenced to the values obtained from a head-sized phantom, including corrections for coil loading, receiver gain, and metabolite T1 and T2 values from the H-1 MRS study in healthy elderly for gray and white matter.10
To obtain atrophy corrected metabolite intensities and to verify that metabolic changes were not an artifact of partial voluming, all MRSI data in this study were analyzed for variation in voxel
compositions in terms of GM, WM, and CSF using SID software developed in house. This computation method analyzed the tissue segmented images. These tissue segmented images were co-registered with the MRSI data to estimate the composition of tissue in the MRSI voxel.11,12 The computation was performed with the consideration of the MRSI point spread function, chemical shift displacement effects, and signal sensitivity across the PRESS volume, which was determined experimentally on a head sized phantom.12 Assuming that no metabolites are observed in CSF, the composition of each MRSI voxel was characterized by the two parameters tissue content p=(GM + WM) and gray matter index f=GM/(GM+WM). Then, atrophy corrected metabolite intensities, i.e. of NAA, were computed according to NAA= NAA* 1/p, with NAA* being the uncorrected intensity and "f" was used as covariate for the statistical analysis to determine the extent to which tissue composition contributed to metabolic differences.13
Typically, MRSI spectra from the hippocampal-body exhibited a better spectral resolution than spectra from hippocampal-head which often suffered from poor B0 field homogeneities in this region.14 Furthermore, hippocampal-head contained more GM tissue instead of the voxels from the tail, reflecting the brain anatomy in this region. So, MR spectra from the hippocampus-body were used exclusively for the further analysis.15
Statistical Analysis: Repeated measures of analyses of variance (ANOVA, SA Institute Inc. Cary, NC) were used to test group differences of hippocampus volumes and grossly atrophy measures. Analysis of covariance (ANCOVA, BMDP Statistical Solutions Inc. Ireland) was used with tissue composition f as covariate, to test by group and side by side differences of metabolite concentrations in right and left hippocampus.16 In order to determine the extent that hippocampal NAA and volume were independent of each other, NAA was used as the independent variable. Hippocampus volume was used as covariate with NAA to determine their group membership by
Informatica Medica Slovenica 2005; 10(1)
41
ANCOVA.17 Results were expressed as mean ± s.d. with standard error unless otherwise indicated. The level of significance for the differences was p
< 0.05.
Comparison between neocortical epilepsy (NE) and mesial lobe epilepsy (mTLE) patients
Epilepsy Patients and control subjects: Ten
subjects with available data on medically refractory NE (based on previous seizure semilogy and ectal EEG recording) were studied. The age range was 25-51 years (mean age 34.4 ± 7.4 years). All patients were first evaluated at the Epilepsy centers. At epilepsy center, the seizure focus was localized by scalp (including sphenoid electrodes) as necessary subdural electrode recordings.18 Only patients were included whose ictal recordings were demonstrated either by localized voltage attenuation or rhythmic sharp activity that preceded or coincided with the onset of the clinical seizure. Among these patients, four had their seizures arose from the frontal lobe neocortex, two from the occipital lobe neocortex, one from the parietal lobe neocortex and three had their seizures arise from the lateral temporal lobe neocortex. The severity and frequency of seizures were similar in the NE patients and mTLE patients who were used for study. The NC and mTLE patients consisted of 23 unilateral medically refractory mTLE patients. The age range was 1449 years (mean age 35 ± 9.7 years). Both NE and mTLE patients were compared to 16 healthy, age and sex matched controls (mean age range 23-56 years; mean age 33.3 ± 7.9 years).
Diagnostic MRI and MRSI analysis: All the NE
patients had normal MRI. Both hippocampi were normal in appearance, shape and volume. In 8 out of 10 patients absolute concentrations were concluded using the unsuppressed water resonance as a reference. The concentration of metabolites included metabolite-corrections and water signals for T1 and T2 relaxation, and corrections for the number of protons belonging to the metabolite resonances. EEG findings were used as a standard for localization and
lateralization of the seizure focus. Lateralization by MRSI was performed in several ways: The NAA, choline and creatine concentrations (NAA, Cho and Cr) and metabolite ratios (NAA/Cr, NAA/Cho, and NAA/(Cho + Cr) were compared between left and right hippocampi of the patients. Furthermore, the patient's values were compared to control's values. Asymmetry indicies were
calculated by (MContra-Mipsi)/(M contra
+ Mipsi)100 for patients and (Mlf-MrgJ/Mleft + MrgJ100 for controls, with the metabolite concentration or metabolite ratio designed by M.
Statistical Analysis: Statistical analysis of the MRSI data comparing metabolite concentrations and ratios between NE and mTLE patients and controls were done using a one tailed unpaired t-test. A probability value of p <0.05 was considered significant for each analysis. In order to detect also smaller metabolite changes we did not correct for multiple comparisons. All data are presented as mean ± s.d.
Post-operative mTLE patients' evaluation
Patients and control subjects: 10 patients with unilateral medically refractory mTLE based on seizure semiology and ictal EEG recording, were studied before and after surgery. A temporal lobe resection, including the anterior part of the hippocampus was performed on the ipsilateral side (defined by EEG telemetry, MRI and other clinical measures) in all patients. The age range was 24-49 years (mean age 34.6 ± 9 years). Eight of the patients were seizure free after temporal lobectomy (class I according to Engel's classification;19 2 patients had continued complex partial seizures following surgery (1 patient class II and I patient class III). Memory was evaluated using the Rey Auditory Early Learning Test (RAVLT), a widely used measure of verbal learning and memory. The patients were compared to healthy age and sex matched controls (age range 23-56 years; mean age 33.3 ± 7.9 years).
MRI and MRSI examination: All MRSI studies were performed on a 1.5 Tesla Magnetom VISION
42
Sharma R: Molecular Imaging in Neurodegeneration
(Seimen Erlangen) as previously described. The follow-up measurements were performed using exactly the same measurement protocol. In postoperative studies, the patients were carefully repositioned like the same orientation prior to surgery. The anterior border of the contralateral hippocampus was used pre- and postoperative as a hallmark for positioning of the PRESS box.
MRSI post-processing: The MRSI data were processed as described before. An average of 6 voxels centered primarily on hippocampus gray matter was selected. Metabolite concentrations were found to vary along the long axes of the hippocampus. Therefore, same co-registered voxel positions were postprocessed for pre- and postoperative sessions if the spectrum quality was sufficient at these positions. In addition, the selected voxels within the PRESS box were selected as good as possible at equivalent positions in the hippocampus. This postprocessing procedure excluded the possibility of detecting differences due to changes in metabolite concentrations between the anterior and posterior part of the hippocampus.
Quantitative data were calculated using the unsuppressed water signal as a reference. The corrections required for the quantitation -including corrections for the number of protons and relaxation times have been previously described. Furthermore, to compare also the water signal before and after surgery it was corrected for coil loading by multiplying the signal with the transmitter reference value.
Statistical Analysis: Statistical analysis of the MRSI data were compared for the metabolite concentrations and ratios between patients for the pre-and postoperative sessions. Analysis was done for the patients and controls using ANOVA (SAS Institute Inc.Cary, NC). The p-values were calculated using two tailed paired t-tests and unpaired t-tests for comparisons between pre- and postoperative data and comparison between patients and controls, respectively. However, when comparing the three groups (patients pre-and postoperative and controls) statistical
significance was assumed only for probability values of p <0.05 using the Tukey's test to examine all pair-wise comparisons among means. All data are presented as mean ± s.d.
Combined multislice and PRESS MRSI in mTLE patients: MRSI studies were performed on 9 patients with unilateral mTLE based on ictal semiology and EEG recording, which was defined as ipsilateral side. Seven healthy volunteers were included as controls with sex and age matched data. The MRSI studies were done on a 1.5 Tesla Magnetom VISION (Seimen Erlangen) using a standard circularly polarized head coil.
Diagnostic MRI and MRSI: Multislice MRSI studies require excellent magnetic field homogeneity over a large region. Due to severe susceptibility B0 shifts in the anterior temporal lobe and hippocampus, this region often requires a different shim than other brain areas. Therefore a separate PRESS volume-preselection experiment for the hippocampal region was performed using a separate shim. The slice was angulated parallel to the long axis of the hippocampus, and a circular k-space encoding of 24 points diameter was used (TR/TE=1800/140 ms, nominal voxel: 9 x 9x 15 mm3). The multi-slice experiment included two 15 mm slices acquired using a global shimming with a circular k-space encoding of 36 points diameter (TR/TE=1800/140 ms, nominal voxel size: 8 x 8 x 15 mm3). Both slices were angulated parallel to the optic nerve with the lower slice just including the corpus-callosum and the other slightly above the corpus callosum. Lipid removal was performed by a data post-processing procedure.
Regression against tissue content and histogram analysis: Automated spectral fitting and automated MRI tissue segmentation after co-registration with MRSI were routine methods. Another factor was metabolite differences in GM, WM and WM signal hyperintensities (WMSH). Therefore, a regression analysis was done for the volume corrected (NAA) as a function of the GM tissue fraction within each MRSI voxel. An extrapolation to 100% GM yielded an estimate of NAA in pure GM. Similarly, an extrapolation to
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100% WM yielded NAA in pure WM. Other method was based on the hypothesis that different progressive neurodegeneration stages were reflected by NAA concentration in the voxels across that brain region. For it, histogram analysis of atrophy corrected (NAA) values were obtained from parietal lobes which contained more than 70% GM with assumption that GM was effected maximum in AD.
Table 1 Clinical characteristics of patients with Alzheimer's disease (AD) and control subjects.
	AD	Control
Number of subjects	12	17
Mean Age (yrs)*	72.5 ± 9.8	73.1 ± 5.1
Age Range (yrs)	52 - 80	60 - 82
Women/Men	8 / 4	14 / 3
Mini-Mental State*	16.9 ± 5.6	29.7 ± 1.2
Mini-Mental State Range	14 - 21	27 - 29
Mean duration of the	4.4 ± 1.4	
symptoms (yrs)*		---
Table 2 Atrophy corrected metabolite concentrations of NAA, Cho, and Cr and NAA/Cr and NAA/Cho ratios from right and left hippocampus in AD patients and control subjects. Tissue content p (in percent of the MRSI voxel volume) and gray matter index f of the MRSI voxels positioned at right and left hippocampus, characterizing MRSI partial volume effects.
AD	Control p-value
2884 ± 102 0.003 2943 ± 86 0.001
2.8 ± 0.3 0.03
18.2 ± 0.5 0.01
38.1	± 0.8 0.01
42.2	± 0.6 0.03
1.4 ±0.03 n.s. 1402 ± 52 n.s.
Results
Alzheimer's disease
Demographics: The demographic data is shown in Table 1. Patients and elderly controls were comparable in age (p > 0.5 by ANOVA) and had a similar gender distribution (67% vs 82% females in the patient and control group, respectively). The AD patients had a mean MMSE score of 18.4 ± 5.2 s.d. with the range from 12 to 28 and a mean duration of symptoms of 4.2 years ±1.8 years (mean ± s.d.). Elderly control subjects had MMSE scores of at least 28 or better.
MR Spectroscopic Imaging: Table 2 shows the results of atrophy corrected NAA, Cr and Cho and the ratios NAA/Cr and NAA/Cho from the left and right hippocampus in AD and control subjects. Data shows (% tissue composition in voxel) and f, revealing significant differences in the composition of MRSI voxels between the two groups. After correcting NAA atrophy in the right and left hippocampus in AD, the NAA was reduced by 15% and by 16.2% (p <0.003) respectively, compared to the NAA in control subjects. Variations of tissue composition did not significantly contribute to the differences (p > 0.98). Furthermore, both NAA/Cr, NAA/Cho were significantly reduced (p < 0.02 and p value < 0.03 respectively) in AD compared with elderly controls. Reductions of NAA after NAA correction in AD could not be entirely attributed to the atrophy factor because of the multifactorial dependent nature of NAA. The concentrations of hippocampus Cr and Cho were not significantly different between AD and control subjects. In both groups, NAA, Cr and Cho concentrations were non-significantly higher in the left than in the right hippocampus (p >0.07). MRSI voxels from AD contained an average 10% less tissue than in control subjects, reflecting the increased atrophy in AD. Accordingly, the difference of hippocampal NAA between the groups without atrophy correction (which reflects both NAA and volume changes) was about 40% larger than with correction of atrophy. This emphasizes the
HP-Vol. (mm3)
Right HP-Vol. (mm3) Left
Ventricular CSF (%)
Sulcus CSF (%) White Matter (%) Cortical GM (%) Subcortical GM
(%) TIV (cm3)
1982 ± 134 1868 ± 88 4.2 ± 0.3
23.4 35.2 : 38.8
1.2 ±
1342
t 2.1 t 0.9 t 1.1
0.08
± 35
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Sharma R: Molecular Imaging in Neurodegeneration
importance of correcting MRS and MRSI data for partial volume effects.
Table 3 Normalized hippocampus volumes (HP-Volume) and volumes of cortical and subcortical gray matter (GM), white matter (WM), and fluid (CSF) as percent of total intracranial volume (TIV) in AD patients and control subjects.
Concordant Discordant
Non-lateralized
FDG-PET
Asymmetry score
(>3%) HV (AI > 8%) T2 (AI > 4%) NAA/(Cho+Cr)
(AI > 12%) NAA (AI > 12%)
12
10
9
7 7
MRI Voluming and Segmentation: Table 3 represents the normalized hippocampal volumes in AD patients and elderly controls, as well as percent volumes of cortical and subcortical GM, WM and ventricular and sulcal CSF. As expected, AD patients had smaller hippocampi on both sides than control subjects, on the right 20.1% (p<0.003) and on the left 21.8% (p < 0.001) and the right hippocampus was slightly larger (~2%) than the left, but this difference was not statistically significant (p<0.5). In AD, ventricular and sulcal CSF volumes were enlarged by 34.1% (p < 0.03) and by 22.8% (p > 0.01), respectively, when compared to controls, and cortical GM and WM were reduced by 6.1% (p < 0.03) and by 7.3% (p < 0.01), respectively. In contrast, subcortical GM (p > 0.6) and total intracranial volume (p > 0.06) were not significantly different between AD and control subjects.
Combination of MRI and MRSI measures for AD: Figure 2 shows the distributions of hippocampal NAA< hippocampal volume and% ventricular size in AD and control subjects, separated by gender. It was observed that no single measurement alone provides a complete distinction of AD patient and control subjects. In order to explore this possibility, hippocampal
NAA and volume were analyzed to get better information which may aid group classification as these two measures are independent of each other. After normalizing the group differences in hippocampal volumes by ANCOVA, significant differences of hippocampal NAA between the groups were still observed (p<0.002). Similarly, after controlling for group differences in hippocampal NAA, volume losses in AD remained significant (p<0.001). Taken together, these results revealed that hippocampal NAA and volume each provide independent information relevant to the discrimination of AD from control subjects. Figure 3 shows the distribution of atrophy corrected hippocampal NAA (right and left averaged) from each AD patients and control subjects as a function of their hippocampal volume (right and left averaged). To determine the classification power of hippocampal NAA and volume when combined, we performed a stepwise linear discriminant analysis with hippocampal NAA being the first variable entered. Alone, hippocampal NAA correctly classified 80% (8/10) of the AD and 75% (12/16) of the control subjects. When hippocampal volume was combined with NAA, the two measures increased the power for the classification of AD and control subjects to 90% (9/10) and 94% (15/16) respectively. Combining the two in addition,% ventricular CSF did not improve the classification of AD and decreased the classification of controls.
iSfieSi&l f ' t|
Figure 2 Brain images are shown before postprocessing (top panels) and after postprocessing (bottom panels) to demonstrate tissue analysis by segmentation for white matter, gray matter and CSF.
0
1
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Table 4 Hippocampal lateralization and voluming as a test of discordance is analyzed in terms of metabolites to evaluate asymmetry scores volume (TIV) in AD patients and control subjects.
Metabolite	AD Control % Diff.			p-value
		NAA (mM)		
Right	7.55	± 0.5 10.01:0.6	14	0.0001
Left	7.61	± 0.4 9.82±0.9	12.6	
		Cho (mM)		
Right	2.02	± 0.7 2.08 ± 0.2	1.46	n.s.
Left	2.04	± 0.4 1.89 ± 0.5	3.8	
		Cr (mM)		
Right	7.02	± 0.6 7.75 ± 0.5	4.9	n.s.
Left	7.96	± 0.6 7.49 ± 0.8	3.0	
		NAA/Cr		
Right	1.08	± 0.8 1.3 ± 1.2	9.2	n.s.
Left	0.96	± 0.6 1.3 ± 1.1	15.0	
		NAA/Cho		
Right	3.74	± 0.7 4.8 ± 3.0	12.4	n.s.
Left	3.73	± 1.0 5.4 ± 1.8	18.2	
	Tissue content p (%)			
Right	84	± 3 98 ± 2	7.6	0.005
Left	87	± 3 96 ± 3	4.9	
	Gray matter index (f)			
Right	0.45	± 0.03 0.55 ± 0.05	10	0.0001
Left	0.62 :	t 0.02 0.62 ± 0.04	0	
HV for 19 control subjects (38 hippocampi) was 3452 mm3. Although, the mean HV of the right lobe (3501 mm3) was greater than the mean HV of the left lobe (3401 mm3) hippocampi, the difference was not statistically significant (p <0.037). No right-left differences were found for T2 and H-1 MRSI measures.
Table 5 Lateralization sensitivity in TLE patients based on HV defined hippocampal atrophy.
	Atrophy	Without atrophy
	(n=12)	(n=7)
FDG-PET	11 (90%)	5 (70%)
NAA/(Cho + Cr)	10 (82%)	5 (70%)
NAA	10 (82%)	4 (60%)
Patients: Table 5 represents the ictal EEG and imaging results for the 5 patients. Results were represented as lateralized for each modality and these were based on generally conservative criteria for intermodality comparison of lateralization for each patient.
Table 6 Comparison of lateralization in Temporal Lobe Epilepsy (n=15) as test of discordance by using hippocampal volume, metabolites peaks.
Concordant Discordant
Non-lateralized
FDG-PET
Asymmetry score
(>3%) HV (AI > 8%) NAA/(Cho + Cr)
(AI > 12%) NAA (AI > 12%)
20
15
14
15
0
3
Figure 3 Coronal images showing distribution of atrophy corrected hippocampal NAA (right and left averaged), Cr and Cho from each AD patients and control subjects as a function of their hippocampal volume.
4.2 Epilepsy: Controls: Table 4 represents the mean values for each of the quantitative MR measures: hippocampal volume (HV), T2 relaxometry, NAA, NAA/(Cho + Cr). The mean
Metabolite measures for optimum lateralization in Epilepsy: In order to determine the best lateralization criteria, the concentrations of NAA, Cr and Cho, as well as metabolite ratios of NAA/(Cr+Cho), NAA/Cr, and NAA/Cho were evaluated. Statistical analysis showed no significant left to right differences in any metabolite concentrations or metabolite concentrations or metabolite ratio in the control group. In control and patient groups, no age- or
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sex-related differences were found (values p > 0.05). In the TLE patients the NAA ratios and the NAA concentrations (NAA) observed as substantially decreased in the ipsilateral hippocampus. In addition, NAA measures were decreased in the contra-lateral hippocampus of TLE patients in comparison with controls. Hippocampal (NAA) was 11.6 ±1.3 mmol/L (mean ± 1 s.d.) in controls, 8.1 ± 1.5 mmol/L (p < 0.002) on the ipsilateral side, and 9.4 ± 1.3 mmol/L (p > 0.0001) on the contralateral side of TLE patients. Similarly, the mean NAA/(Cr + Cho) ratio was found to be 0.81 ± 0.06 in controls, 0.059 ± 0.1 (p < .004) in the hippocampus ipsilateral to the seizure focus, and 0.73 ± 0.13 (p>0.05) in the contralateral hippocampus in patients. The concentrations of Cr and Cho were not significantly changed in patients compared with the controls (p > 0.08). However, a trend for decreased [Cr] was found, with the mean ipsilateral [Cr] in TLE patients 10% less than in controls. The coil-loading metabolite signals yielded a slight increase in the ipsilateral Cho signal of 6.7% in patients, but no statistical significant difference was observed. Furthermore, although a decreased ipsilateral Cr/Cho was found for the TLE patients (ipsilateral mean Cr/Cho=0.90, control mean Cr/Cho = 1.0). Lateralization analysis based on Cr/Cho was failed in 8 of 16 patients.
FDG-PET atrophy information, lateralization, and MRI findings is presented in Table 5. The table shows MRSI lateralization based on [NAA] and NAA/(Cr + Cho). Lateralization criteria included comparing left to right MRSI measures in TLE patients and TLE MRSI data compared with control data. The NAA/(Cr+Cho) ratio provided concordance with EEG lateralization in left to right comparison in all TLE patients. In 15 of 16 patients, both ipsilateral [NAA] and NAA/(Cr + Cho) mean ± s.d. values were less than mean ± 1 s.d. control values. The [NAA] did not lateralize correctly in 3 patients without atrophy, but showed greater bilateral abnormalities (i.e. abnormalities in both the ipsilateral and contralateral hippocampi) in comparison with control [NAA] values. Similar observations were
common for the metabolite ratio measures. The NAA/Cho ratio failed to lateralize one patient correctly, and NAA/Cr failed to lateralize 3 patients. The NAA/(Cr+Cho) ratios and [NAA] values for ipsi-and contralateral abnormalities in TLE patients and left vs right comparison in controls are plotted as shown in images Figure 4 and 5.
#
v i O
v mm
Figure 4 T1 axial images showing VOI and spectroscopic voxels and selective spectral peaks from right and left sides to compare metabolites in cortical and ventricular regions (peaks on right panels).
Relationship of decreased NAA to hippocampal atrophy: The second specific aim of this study was to determine if decreased hippocampal NAA in TLE is simply due to hippocampal atrophy. Figure 5 and 6 shows that 6 out of 7 subjects without hippocampal atrophy, as assessed by MRI, had decreased ipsilateral hippocampal [NAA]. These findings strongly suggest that decreased hippocampal NAA cannot be explained by atrophy alone. Because the water signal was used for absolute quantitation, the magnificent of the hippocampal water signal was analyzed. The TLE patient groups showed slightly increased mean ipsilateral hippocampal water signals (mean increase in patients with hippocampal atrophy:
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7.9%; without hippocampal atrophy: 4.4%). This might be due to either increased cerebrospinal fluid water or increased T2 of water in the hippocampus. However, the mean ipsilateral water signal increase of 7.9% in TLE patients with atrophy accounted for only 6.5% of the 34.2% [NAA] decrease. Furthermore, no negative correlation between the water and the NAA signal was observed (correlation coefficient 0.26).
NAA/Cho ratios were below ±1 s.d. of the controls in 8 of 16 (50%) TLE patients.
A
B
C
D
E
Figure 5 The prediction power of MRI (column A), NAA images (column B) and MRSI (for left vs right lateralization on columns C-D). Metabolite images are demonstrated with VOI box in column D on left vs right voxels selected.
Bilateral abnormalities: To determine the presence of bilateral hippocampal disease in TLE, MRSI data from the contralateral hippocampus were compared to controls. Mean contralateral [NAA] was significantly reduced below controls. Mean contralateral [NAA] was less than ± 1 s.d. below the control mean in 12 of 16 (75%) TLE patients. The NAA/(Cr + Cho), NAA/Cr and
Figure 6 Data is shown for TLE epilepsy subjects without hippocampal atrophy, as assessed by MRI, had decreased ipsilateral hippocampal [NAA] on y axis.
Relationship of MRS data to surgical outcome
in Epilepsy: One of the aims of these MRSI and MRI examination was to investigate the correlation between MRSI findings and seizure surgery outcome. 11/16 patients underwent surgery. Six patients with hippocampal atrophy had surgery and 5 became seizure free (five class I, one class III). Both [NAA] and [NAA/(Cr+Cho)] in these 6 subjects provided lateralization concordant with EEG. 5 patients without hippocampal atrophy had surgery. In two of them, [NAA] and [NAA/(Cr + Cho)] findings were concordant with EEG and they became seizure free (class I). In three remaining patients the NAA/Cr + Cho ratio lateralized concordant with EEG but NAA was equally low on both sides or was even lower on the contralateral side. These three patients continued to have seizures after surgery although the seizure frequency decreased. 4 of the 7 seizure-free patients also showed contralateral decreased [NAA] for the patients who were followed after seizure surgery as shown in Figure 5.
In summary, 7 unilateral TLE patients who underwent surgery, a contracordant decrease of
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[NAA] and [NAA/(Cr + Cho)] in the ipsilateral hippocampus predicted the surgical success. 5 of these 7 patients had ipsilateral atrophy. In three subjects without ipsilateral atrophy, a bilateral equally decreased or contralateral lower [NAA] was associated with a poor surgical outcome.
Lateralization of lobes in Epilepsy: FDG-PET: 23 of 24 (96%) patients studied with PET had one or more regions with relative hypometabolism confined to a single TL. Because of the subjective nature of determining relative hypo-metabolism and the risk of false interpretation in the presence of asymmetric partial volume effects, several asymmetry thresholds were evaluated. PET with MRI co-registration was necessary to resolve lateralization in one patient. Requiring 3 or more regions of relative hypo-metabolism was a conservative but consistently reliable threshold for lateralization that resulted in 21 of 24 (88%) patients lateralized by FDG-PET with no discordance.
Diagnostic MRI: MRI revealed easily discernable relative hippocampal atrophy in 15 patients (66%) and unilateral increase in hippocampal T2 signal in nine patients (36%). None of these patients shows T2 hypersensitivity without hippocampal atrophy. In all cases MRI identified hippocampal atrophy was concordant with ictal EEG. All 25 patients had hippocampal examination. An atrophy index (AI) > 8% was caliberated as criteria to eliminate all discordance. With this threshold value of AI, 17/25 patients (68%) were lateralized. In 16 of 17 patients had concordant hippocampal atrophy more than ± 2 s.d. below the mean of control. To determine if the yield for accurate lateralization could be improved, a presumption was added that HV can be lateralized only if hippocampal volumes were more than ± 2 s.d. below the mean of controls. This presumption decreased the percent discordance for all atrophy index thresholds < 8%, but percent concordance remained lower. Thus, the optimum criterion for lateralization with hippocampal atrophy was an atrophy > 8% without any additional requirement for absolute abnormality.
T2 relaxometry: To eliminate all discordance an atrophy index > 4% was required. With this threshold, 9 of 13 patients (69%) were lateralized. If the presumption was added that T2 are lateralized only if it was more than ± 2 s.d. above the mean of controls, discordance was eliminated at all atrophy index values with the trade-off that sensitivity was decreased to 54%. Thus, the optimum criteria for T2 lateralization was atrophy index values, but it showed less sensitivity decreased to 54%. Thus the optimum criteria for T2 lateralization was atrophy index > 4% without any additional presumption for absolute abnormality.
H-1 MRSI: Both NAA/(Cr+Cho) and NAA included some discordance patients with relatively high asymmetry. An atrophy index > 16% was required to eliminate all discordance with NAA/(Cr+Cho). This resulted in 10 of 24 patients (42%) lateralized. Adding the requirement that NAA/(Cr + Cho) be lateralizing only if it was more than ± 2 s.d. below the mean of controls did not reduce the atrophy index that was required to eliminate discordance. With [NAA] lateralization, an atrophy index > 24% was required to eliminate all discordance, a threshold that resulted in a sensitivity of only 21%. AD with NAA/(Cr + Cho), adding the requirement that [NAA] be lateralizing only if it was more than ± 2 s.d. below the mean of controls did not reduce the atrophy index that was required to eliminate discordance. These data suggest that no optimum criteria for H-1 MRSI lateralization exist without including one or more cases discordant with ictal EEG. Because discordance is not necessarily an indication of false lateralization, we examined H-1 MRSI lateralization results in patients completely free of seizures (including auras or simple partial seizures) following surgery. In this subgroup of 15 patients, NAA/(Cr+Cho) discordance was present in 4 patients with atrophy index of 11%, 15% and 1%. None of the fifteen patients had discordant [NAA]. The mean atrophy index of normal controls was ± 2 s.d., approximately 20% for both H-1 MRSI measures. If the asymmetry threshold was based on this value, lateralization sensitivity was low about 27%
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with NAA/(Cr + Cho) and 47% with [NAA] (n=15). Selective lateralization based upon the choosing side with the lowest value, regardless of degree of asymmetry, was unreasonable, even if only patients with abnormal values are counted.
Thus, H-1 MRSI measures the decision of optimized lateralization criteria remains arbitrary. In present study, an atrophy index > 12% seems a reasonable threshold for H-1 MRSI lateralization because NAA/(Cr+Cho) lateralization was simply unreliable with an atrophy index <12% (regardless of whether only patients with abnormal values are included). With this threshold applied to all 24 patients studied with NAA/(Cr+ Cho), the sensitivity was 58%, with discordant lateralization in one seizure free patient. The sensitivity for [NAA] was 63%, with discordant lateralization in one patient (no worthwhile improvement in seizures). Adding the presumption of including patients with H-1 MRSI values ± 2 s.d. below the means of controls only reduced sensitivity and did not increase discordance at lower asymmetry thresholds.

LF	CF	UC	PC
Figure 7 Metabolites in different regions of brain on ipsi and contralateral side (left) are compared in different locations of cortex (on right) of brain as the basis of asymmetry analysis and decision-making in mTLE epilepsy. Data is shown in Tables 7 and 8. Different parts of frontal lobe (LF), medial cortex (MC), subcortical (SC), parietal cortex (PC) is shown on brain image A (on right side).
Comparison of lateralization in mTLE:
Comparison of lateralization was performed in
those patients who completed all examinations included in the evaluation study. Table 7 represents the lateralization from the 23 patients who completed FDG-PET, hippocampal atrophy and H-1 MRSI studies in all 25 patients. Using criteria described above, lateralization sensitivity was 87% with FDG-PET, 65% with hippocampal volumetry, 61% with NAA/(Cr+Cho) and 57% with [NAA]. With these criteria no discordance was found with FDG-PET or hippocampal voluming. Two patients were discordant with H-1 MRSI measures, one with NAA/(Cr+Cho) and the other with [NAA]. Table 7 represents the lateralization in patients grouped according to the presence or absence of hippocampal atrophy defined by hippocampal voluming. FDG-PET lateralized all but one (93%) of the 15 patients with hippocampal atrophy; each of the H-1 MRSI measures lateralized 10 (75%). NAA/(Cr + Cho) lateralization was discordant in one patient who has been seizure free since seizure surgery. Of the 8 patients without hippocampal atrophy, FDG-PET lateralized 6 (75%), NAA/(Cr + Cho) 4 (50%) and [NAA] 3 (38%) as shown in Figure 5 in 4-6 rows. The [NAA] lateralization was discordant in one patient who has had no worthwhile improvement in seizures since surgery. This patient was not lateralized by FDG-PET or NAA/(Cr + Cho). Utilizing a combination of MR measures by adding the H-1 MRSI lateralized patients without hippocampal sensitivity of 87%, a value equal to that of FDG-PET. Figures 7 and 8 demonstrated the ability of the combined MR techniques to lateralize patients even in the absence of hippocampal atrophy. In 13 patients who underwent T2 relaxometry, lateralization was observed in 69%, as compared to 100% of these patients with FDG-PET, 77% with hippocampal volumetry, 62% with NAA/(Cr+Cho) and 69% with [NAA] shown in Table 7. 3 patients with unilateral hippocampal atrophy had abnormal T2 relaxometry. No patients with normal hippocampal volumes had abnormal T2 relaxometry.
Detection of bilateral abnormalities:
Abnormality was defined as ± 2 s.d. from the mean of controls. Of the 24 patients available for
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comparison with hippocampal volumetry and H-1 MRSI, bilateral abnormalities were detected in 4 patients with HV (17%), 8 with NAA/(Cr + Cho) in 33%, and 7 with [NAA] in 29%. Of the 13 patients available to compare T2 relaxaometry, bilateral abnormalities were detected in 3 with hippocampal volumetry (23%), 6 with NAA/(Cr + Cho) in (46%), 4 with [NAA] in 31% and 1 with T2 in 8%.
Table 7 Ipsi- and contralateral NAA concentrations and NAA/(Cr+Cho) ratios (mean ± SD) in the hippocampus of NE and mTLE patients in comparison with controls (p values comparison in patients with controls).
NAA (mM) NAA/(Cr+Cho)
Controls (n=16)	11.6 ±1.3	0.82 ±	0.06
	NE (n=8/10)		
Ipsilateral	12.3 ± 1.9	0.79 ±	: 0.1
Contralateral	11.4 ± 2.7	0.78 ±	0.1
	mTLE (n=23)		
Ipsilateral	8.5 ± 1.3*	0.62 ±	0.1*
Contralateral	9.6 ± 1.3*	0.72 ±	0.1*
* indicates p value 0.001.
Metabolite imaging and correlation analysis: 24
patients had surgery. The mean follow-up was 20 months (range 15-28 months). 17 patients (71%) were seizure free (Class I); 3 had rare disability seizures (class III); and 2 had no worthwhile improvement (class IV). Lateralized hippocampal atrophy detected by hippocampal volumetry and T2 relaxometry correlated strongly (p > 0.0001) with seizure free outcome. FDG-PET (3 or more regions of relative hypometabolism) tended to correlate (p > 0.07) with good outcome (class I and II). Lateralized H-1 MRSI did not correlate with outcome.
Bilateral MR abnormalities also did not correlate with outcome. 3 patients had bilateral hippocampal artrophy. Two were seizure free and one had a class III outcome. 5 out of 7 patients with bilaterally abnormal NAA/(Cr + Cho) and 5 of 8 patients with bilaterally abnormal [NAA] were seizure free. The one patient with bilaterally abnormal T2 relaxometry was seizure free.
Table 8 NAA and NAA/(Cr+Cho) asymmetry indices (mean ± SD) of NE and mTLE patients in comparison with controls (p values compared with controls).
Asym. Asym.
NAA NAA/(Cr+Cho)
Abs. asym.
NAA
Abs. asym.
NAA/(Cr+Cho)
Contr.	-0.2±4.85	0.63±6.25	3.15 ±5.65	4.75±3.95
NE	-4.6±7.8	—1.2 ±8.01	5.71±6.8	6.75±3.8
	n.s.	n.s.	n.s.	n.s.
mTLE	7.45±8.27	7.4±8.65	9.2±6.4	9.07±6.7
	p<0.001	p<0.003	p<0.001	p<0.04
Neocortical patients: Table 8 shows for NAA and NAA/(Cr+Cho) of neocortical epilepsy, mTLE and controls. In addition, [Cr} and [Cho] were calculated. The mean ipsilateral values for NAA, Cr, Cho in the neocortical patients was 12.3 ± 1.9, 9.8 ±1.2, 2.9 ± 0.7 respectively and for the mTLE patients 8.3 ± 1.3, 8.6 ± 1.6, 2.6 ± 0.4.
In contrast to mTLE, there were no significant changes in any ratio or metabolite concentrations in neocortical epilepsy compared to controls as shown in Table 8. In Figure 5, ipsilateral and contralateral values of NAA and NAA/(Cr+Cho) ratios are represented for the individual patients. In 19 out of 23 mTLE patients the ipsilateral [NAA] and NAA/(Cr+Cho) values were lower than the contralateral values. In contrast there was no uniform tendency between ipsi- and contralateral values for neocortical patients. A comparison of ipsilateral values with controls shows that for mTLE, ipsilateral [NAA] and NAA/(Cr+Cho) are significantly reduced (p < 0.001), whereas the mean values in neocortical epilepsy were not different from controls. Furthermore, Figures 7 and 8 showed that mTLE patients also had significantly reduced values in the contralateral hippocampus. [NAA] values and NAA/(Cr+Cho) ratios were reduced at least by ± 1.0 s.d. below the control mean in 16 patients (70% of all patients, p> 0.0001) and in 13 patients (57%, p < 0.002) respectively.
The asymmetry index (AI) was calculated to determine the degree of asymmetry between ipsi-and contralateral values of NAA and
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NAA/(Cr + Cho) in neocortical epilepsy as compared to controls and mTLE shown in Table 8. In contrast to mTLE, the asymmetry of the neocortical epilepsy group did not differ significantly from controls. To detect any asymmetry independent of the location of the seizure focus, between left and right side in neocortical patients, absolute asymmetry indices were calculated. Although these asymmetry values were slightly higher in neocortical epilepsy than in controls, this difference was not significant, in contrast to the highly significant difference between mTLE and controls. Figures 6-8 represent the ipsilateral values of NAA and NAA/(Cr + Cho) for neocortical epilepsy and mTLE patients. For single patient comparison values more than ± 2 s.d. from either group (NE or mTLE) were considered to be abnormal. Ipsilateral [NAA] were reduced in 13 out of 23 mTLE patients more than ± 2 s.d. below the mean value of NE patients. On other hand, 6 out of 8 NE patients had higher [NAA] and 4 out of 10 NE patients had NAA/(Cr + Cho) values more than ± 2 s.d. above mTLE. Therefore, despite the overlap between the two groups, the metabolite values may help discriminate mTLE from NE.
Regional Variations: To observe if the location of the focus influenced the NE metabolite values, the patients were divided into 3 groups based on the relationship of their seizure focus to the hippocampus. Patients whose seizures arose from the ipsilateral frontal lobe were examined separately because of the rich, profuse ends preferred pathways between the frontal lobe and the medial temporal lobe frontal structures. All other patients were grouped together since the location of their seizure foci would be expected to have less influence on the hippocampus. No relationship between the distances of the seizure focus from the ipsilateral hippocampus (either anatomical or in terms of preferential pathways) was found for the metabolites as shown in Figure 4. Two patients without convulsions were not multiple comparisons. distinguishable from the others with respect to NAA, or NAA/(Cr + Cho) ratios.
Multislice technique: In 7 out of 9 patients (in 78%) the lateralization by MRS was concordant with EEG. NAA/(Cr+Cho) was significantly decreased in both ipsilateral and contralateral hippocampi compared to controls (p < 0.005). Furthermore, the asymmetry between ipsilateral and contralateral regions was highly increased (p <0.001) compared to controls). Four patients (44%) had bilateral reductions in NAA/(Cr+Cho) more than ± 2 s.d. below control mean. These results confirm previous findings of reduced hippocampal NAA ipsilateral and contralateral to the seizure focus. The Figure 1 summarizes the findings of multislice study of all brain examinations. A total of 18 voxels in gray matter (9 different brain regions) were analyzed. In total, 27 ipsi-, contralateral, ipsi-control, contralateral-control examinations were tested by tail t-tests without correction for multiple comparisons. These comparisons were performed and showed in 2 voxels with tendency of reduced NAA/(Cr + Cho) on one side. However, there was no significant difference when corrected for multiple comparisons.
Discussion
A strategy of metabolite imaging and its power of prediction by using MRSI is presented. Based on MRI volume measurements were not really specific indicator of neuronal integrity. The reason is because the volume loss may reflect a variety of nonspecific mechanisms of neurodegeneration including neuronal loss, shrinkage of neuron cell bodies, or even neuronal changes such as loss of glial components. Furthermore, volume reductions may be attenuated to the extent that neurons are replaced by glia. However, the pathological neuronal loss is often accompanied by reactive gliosis.
NAA has been described as a neuronal marker because of its exclusive high concentrations in neuron cells. However, diminished NAA levels cannot be unambiguous attributed to neuronal loss. It may also reflect the dysfunction of neuronal
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metabolism. Earlier reports of reversible decrease of NAA after treatment of patients with amyotrophic lateral sclerosis (ALS) or TLE are suggestive of the NAA as better indicator of neuronal loss or dysfunction than volume measurements.20 In the case of gliosis, it reduces neuron density. It should cause further reduction of NAA levels. This presumption was in contrast to the volume measurements by MRI or metabolic rate measurements with positron emission tomography (PET) which reflect changes of both neurone and glial cells.21 Thus, a quantitative measurement of NAA by H-1 MRSI seems to offer useful assessment of neurodegeneration.
H-1 MRSI enables the measurement of MR metabolite spectra simultaneously from multiple areas in the brain. Conventional single voxel MR spectroscopy methods are limited to a single region of interest at a time. So, H-1 MRSI is of particular importance in investigation of diffused disease such as AD showing heterogenous regional distribution of pathology. H-1 MRSI is a combination of chemical shift metabolite spectra in homogeneous magnetic field and MRI of spatial phase-encoding by stepwise increasing or decreasing magnetic field gradient pulses after slice-selection for planar MRSI.22 After Fourier transformation of the data, each of the major peaks can then be displayed as an image, co-registered to MRI or other neuroimaging methods. H-1 MRSI technique suffers from low sensitivity and long acquisition time. These factors restrict the data sampling to a limited number of phase encoding steps. The resultant truncated sampling causes poor spatial resolution due to effect of "voxel bleeding"23 as a result of contamination of brain metabolite MR spectra with intense lipid resonances originated from outside the brain i.e. skull.
The primary limitation of volume preselection using PRESS is that outer cortex of the brain is difficult to sample and only one slice is studied at a time. Multislice MRSI extends coverage to regions of the outer cortex, using spatially localized saturation pulses to suppress the resonances from skull lipids. However, a problem of these
saturation pulses is that they may also affect metabolite resonances in the outer cortex, complicating the quantitation of metabolite concentration in this region. However, data processing method was reported accomplished by extrapolating the measured spatial frequency distribution selectively from the skull lipids to a wider range. It resulted with less "voxel bleeding" for these lipid resonances and less contamination to generate better brain metabolite images.24
Alzheimer's disease: Noninvasive diagnosis of AD has been a partial success story by using H-1 MRSI demonstrating global cerebral atrophy and reduced hippocampus valumes. However, there is reported overlap between AD patients and nondemented elderly subjects.25
In present paper, H-1 MRSI study of supraventricular regions in the fronto-parietal brain in AD showed reduced NAA levels when compared with controls. NAA reduced levels remain independent of unpredictable tissue composition measured by MRI, which also corroborates with earlier reports.
Data correction for tissue atrophy and for variations of tissue composition provides the opportunity that NAA reductions in AD occur independently from structural variations as measured by MRI. In both right and left hippocampus regions in AD, atrophy corrected NAA levels were reduced when compared with age-matched controls. Earlier, such reduced NAA levels and hippocampal volumes were suggestive of hippocampal lateralization as good noninvasive
tool.25
So, hippocampal NAA measured by H-1 MRSI combined with measurements of hippocampal volume by MRI may improve diagnosis.24,25 Similarly, outer cortex regions in AD were analyzed for reduced NAA levels in GM of parietal and frontal cortex by regression method using extrapolation when compared with controls shown in Figure 8. These findings were further confirmed by histogram distribution of the NAA levels in parietal cortex.
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might also be reduced in other regions of hippocampus.33 It suggests metabolite change or neuronal damage. Multislice H-1 MRSI predicted well the regional differences of NAA/(Cho + Cr) ratio of the hippocampus i.e. lower ratio on the ipsilateral side of the seizure focus than the contralateral side in the present study. Moreover, many ipsilateral brain regions of TLE patients showed lower values of NAA/(Cho + Cr) and averaged NAA/(Cho + Cr) than in the corresponding brain regions of control subjects. This may be attributed that repeated seizures originating from the hippocampus may eventually lead to impairmrnt of other brain regions.34 This information from multislice H-1 MRSI might help the lateralization of the seizure focus.
Figure 8 Metabolite status of NAA and NAA/(Cr+Cho) in mTLE and neocortical epilepsy is shown in ipsi- and contralateral sides of brain.
Epilepsy: In TLE, the seizure focus was localized in hippocampus.26 For surgical treatment of TLE, the determination of location and size of the focus is important for knowing the extent of affected other brain regions.27 MRI, PET and H-1 MRS are methods of choice to evaluate TLE.28 H-1 MRSI using PRESS was commonly used to measure metabolite changes in regions of the mesial temporal lobe, including hippocampus.29-31 Present paper highlights the importance of lateralization of hippocampus (affected by seizure) using spectra from left and right hippocampi with depleted NAA intensity. These NAA levels were depleted in ipsilateral hippocampus and temporal lobe of TLE patients. However, TLE patients had reduced NAA levels in the hippocampus contralateral to the seizure focus, when compared with controls. It can be attributed due to bilateral mesial temporal sclerosis or due to repeated seizure activity.32 If reduced seizure activity is responsible for reduced NAA levels at the contralateral side, the NAA
Figure 9 Regression analysis of metabolites and tissue fraction analysis for NAA and Cr, cho and white and gray matter in AD.
MRSI data analysis by "voxel selection" uses anatomical landmarks obtained from MRI. Due to operator bias and reading variability, voxel selection becomes complicated especially in diffused diseases like Alzheimer's disease. Multislice H-1 MRSI with completely automated spectral fitting and co-registered MRI segmentation appears free from operator-dependent variability. The method has power of tissue volume correction and quantitation of
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Sharma R: Molecular Imaging in Neurodegeneration
metabolites and tissue composition. Despite it, the method suffers from a number of limitations. First, H-1 MRSI slices are not contiguous and method uses long echo times more than 130 msec. However, shorter echo time is associated with increased lipid and water contamination. In order to suppress lipids and water contamination, very uniform magnetic field homogeneity across the brain is needed. In brain lower-anterior regions of the brain shimming is a problem. However, H-1 MRSI is practical, feasible and provides metabolic and anatomical information from MRI useful in clinical decision-making.
Acknowledgements
Author acknowledges the facility and visiting faculty fellowship provided by Professor Michael W. Weiner at DVA Medical Center, San Francisco, Magnetic Resonance Spectroscopy Center, University of California, San Francisco CA.
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8.	Cendes F,Andermann F, Dubeau F,Matthews PM,Arnold DL Normalization of neuronal metabolic dysfunction after surgery for temporal lobe epilepsy. Evidence from proton MR spectroscopic imaging. Neurology.
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9.	Chang L,Ernst T, Relationships among brain metabolites, cognitive function, and viral loads in antiretroviral-naive HIV patients Neuroimage. 2002 Nov;17(3):1638-48.
10.	Bottomley PA, Selective volume method for performing localized NMR spectroscopy US patent
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11.	Young K, Govindraju V, Soher BJ, Maudsley AA. Automated spectral analysis III: application to in vivo proton MR spectroscopy and spectroscopic imaging. Magn Reson Med 1998; 40,812-815.
12.	Soher BJ, Vermathen P, Schuff N, et al. Short TE in vivo (1)H MR spectroscopic imaging at 1.5 T: acquisition and automated spectral analysis. Magn
Reson Imaging. 2000;18(9):1159-65.
13.	Soher BJ, Young K, Govindraju V, Maudsley AA Representation of strong baseline contributions in 1H MR spectra Magn Reson Med. 2001;45(6):966-72.
14.	MacKay S, Ezekiel F, Di Sclafani V, Meyerhoff DJ et al. Alzheimer disease and subcortical ischemic vascular dementia: evaluation by combining MR imaging segmentation and H-1 MR spectroscopic imaging. Radiology. 1996;198(2):537-45.
15.	MacKay S,Meyerhoff DJ,Constans JM et al. Regional gray and white matter metabolite differences in subjects with AD, withsubcortical ischemic vascular dementia, and elderly controls with 1H magnetic resonance spectroscopic imaging Arch Neurol 1996;53; 167-174.
16.	Schuff N, Amend D,Ezekiel, Steinman SK,Tanabe J et al. Changes of hippocampal N-acetyl aspartate and volume in Alzheimer's disease. A proton MR spectroscopic imaging and MRI study Neurology
1997; 49(6);1513-1521.
17.	Schuff N, Amend D,Meyerhoff DJ et al. Alzheimer disease: quantitative H-1 MR spectroscopic imaging of frontoparietal brain.Radiology.
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18.	Ende GR, Laxer KD, Knowlton RC, Matson GB,et al Temporal lobe epilepsy: bilateral hippocampal metabolite changes revealed at proton MR spectroscopic imaging. Radiology. 1997;202(3):809-17.
19.	Vermathen P, Ende G, Laxer KD, et al. Temporal lobectomy for epilepsy: recovery of the contralateral hippocampus measured by (1)H MRS Neurology. 2002 Aug 27;59(4):633-6.
20.	Mielke R, Schopphoff HH, Kugel H, Pietrzyk U, et al. Relation between 1H MR spectroscopic imaging and regional cerebral glucose metabolism in Alzheimer's disease. Int J Neurosci. 2001;107(3-4):233-45.
21.	Constans JM, Meyerhoff DJ, Gerson J, et al. H-1 MR spectroscopic imaging of white matter signal hyperintensities: Alzheimer disease and ischemic vascular dementia.Radiology. 1995;197(2):517-23.
22.	Block W, Traber F, Kuhl CK, Fric M, Keller E, et al. 1H-MR spectroscopic imaging in patients with clinically diagnosed Alzheimer's disease] Rofo Fortschr Geb Rontgenstr Neuen Bildgeb Verfahr.
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23.	Kuzniecky R, Palmer C, Hugg J, Martin R,et al. Magnetic resonance spectroscopic imaging in temporal lobe epilepsy: neuronal dysfunction or cell loss? Arch Neurol. 2001;58(12):2048-53.
24.	Serles W, Li LM, Antel SB, Cendes F,et al. Time course of postoperative recovery of N-acetyl-aspartate in temporal lobe epilepsy.Epilepsia.
2001;42(2):190-7.
25.	Capizzano AA, Vermathen P, Laxer KD,et al. Temporal lobe epilepsy: qualitative reading of 1H MR spectroscopic images for presurgical evaluation.Radiology. 2001;218(1):144-51.
26.	Li LM, Cendes F, Andermann F, Dubeau F, Arnold DL. Spatial extent of neuronal metabolic dysfunction measured by proton MR spectroscopic imaging in patients with localization-related
epilepsy. Epilepsia. 2000;41(6):666-74.
27.	Vermathen P, Laxer KD, Matson GB, Weiner MW. Hippocampal structures: anteroposterior N-acetylaspartate differences in patients with epilepsy and control subjects as shown with proton MR spectroscopic imaging.Radiology. 2000;214(2):403-10.
28.	Serles W, Li LM, Caramanos Z, Arnold DL, Gotman J. Relation of interictal spike frequency to lH-MRSI-measured NAA/Cr. Epilepsia. 1999 ;40(12):1821-7.
29.	Hsu YY, Chang C, Chang CN et alProton MR spectroscopy in patients with complex partial seizures: single-voxel spectroscopy versus chemical-shift imaging. AJNR Am J Neuroradiol.
1999;20(4):643-51.
30.	Kuzniecky R, Hugg JW, Hetherington H et al. Relative utility of 1H spectroscopic imaging and hippocampal volumetry in the lateralization of mesial temporal lobe epilepsy. Neurology. 1998 Jul;51(1):66-71.
31.	Stanley JA, Cendes F, Dubeau F, Andermann F, Arnold DL. Proton magnetic resonance spectroscopic imaging in patients with extratemporal epilepsy. Epilepsia. 1998;39(3):267-73.
32.	Li LM, Cendes F, Bastos AC, Andermann F et al. Neuronal metabolic dysfunction in patients with cortical developmental malformations: a proton magnetic resonance spectroscopic imaging
study.Neurology. 1998;50(3):755-9.
33.	Cendes F, Caramanos Z, Andermann F, et al. Proton magnetic resonance spectroscopic imaging and magnetic resonance imaging volumetry in the lateralization of temporal lobe epilepsy: a series of 100 patients. Ann Neurol. 1997;42(5):737-46.
34.	Achten E, Boon P, Van De Kerckhove T et al. Value of single-voxel proton MR spectroscopy in temporal lobe epilepsy. AJNR Am J Neuroradiol.
1997;18(6):1131-9.
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Sharma R: Sodium Weighted Clinical Brain Magnetic Resonance Imaging
Research paper ■
Sodium Weighted Clinical Brain Magnetic Resonance Imaging at 4.23 Tesla and Inversion Recovery Pulse Sequence
Rakesh Sharma
Abstract. Application of advanced 4.23 T MRI clinical imager was demonstrated for sodium magnetic resonance (MR) data acquisition. Primary hypothesis: Sodium [Na] in brain is MR visible. Secondary hypothesis was, if, application of multislice spin echo (MSSE) pulse sequence at selected scan parameters can sufficiently visualize the total sodium signal as indicator of sub-clinical activity. A new inversion recovery pulse sequence is proposed for selectively suppressing extracellular sodium to visualize extracellular sodium images. Methods: MSSE pulse sequence technique was used to simulate sodium images of human brain. Phantom of sodium and rat brain were imaged and validated. Results: MSSE pulse technique enabled to visualize the sodium signal at selected scan parameters. Specifically, MSSE pulse technique enabled the identification of different sodium rich areas in the brain, comparable with proton MRI images. Reconstruction images of brain further enhanced the power to classify the brain tissue. Intracellular sodium image of tube phantom were generated by use of inversion recovery pulse sequence. Conclusion: Using MSSE pulse sequence at 4.23 T, in vivo sodium images can be generated in times acceptable for routine clinical brain examination with better sub-physiological information as obtained from proton MRI.
The paper was presentation as finalist Computer Based Medical Systems 2002 award manuscript.
■ Infor Med Slov: 2005; 10(1): 56-72
Author's institution: Departments of Radiology and Medicine, Columbia University, New York, NY 10032.
Contact person: Rakesh Sharma, Departments of Biological Psychiatry, Columbia University, New York, NY 10032. email: rs2010@Columbia.edu.
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Introduction
Integrated approach of Proton MRI with in vivo sodium magnetic resonance imaging seems promising to detect sub-physiological abnormalities. Sodium nuclei inside the brain exist in free and bound forms as extracellular and intracellular sodium populations exhibiting longer and shorter relaxation times respectively reported.1 Sodium moves in free form or also bound with proteins, glycosides via a site localized on the exterior surface of cell membranes. Most of the Na nuclei undergo substantial relaxation before their signals can be acquired. Sodium has a spin of 3/2 and a quadruple moment. So, sodium exhibits many energy transitions and two transverse relaxation times, a long T2 = 16-30 ms and a short T2 = 0.7-3.0 ms depending upon magnetic field strength.2 To acquire sodium images in experimental animals and human, multiple quantum (MQF), single quantum (SQF), triple quantum (TQF) filter methods have been used highlighting the need of high magnetic field at 4.23 Tesla imager as reported.3-7 Several 2D and 3D spin echo and gradient echo pulse sequences have been in use. Until this date, sodium brain images in clinical set up at higher magnetic fields such as 4.23 T have not been reported perhaps due to two reasons: less sodium abundance and less sensitive intracellular sodium nuclei in the body. Present study reports here initial attempt to capture the intracellular sodium images using inversion recovery pulse sequence and demonstration of sodium MR images comparable with proton MR images in order to establish the utility of proton-sodium images by use of double tuned probes. Although, sodium distribution is not identical to the proton distribution in the brain it can vary independently in a multitude of physiological and pathological conditions. For interested readers, comparison of different pulse sequence methods is demonstrated and the source code of sodium imaging inversion recovery is included as Appendix 1 at the end.
Theory of Sodium Imaging and contrast in brain: In the brain, 23Na concentration is 0.14 to
0.16 M in the extracellular space while 0.012 to 0.02 M in the intracellular space about 8-20% of total brain volume. This extracellular space is significant in sodium MR imaging as sodium increases sharply in the diseases like edema. The average concentration of sodium in a volume of brain tissue that comprises both intra- and extracellular compartments is approximately 0.045 M. Integrated approach of proton and sodium MR imaging is significant in evaluating sensitivity and contrast to different brain regions. The contrast solely depends on spin density and relative concentration of sodium and protons in the brain ventricles (filled with cerebrospinal fluid CSF) in the following manner: C = (I — I')/ I', where I is the signal intensity received from the region of interest and I' is the signal intensity received from the background. In the case of brain, region of interest happens ventricles while the background is surrounding brain tissue. Based upon the literature, the concentrations of proton and sodium nuclei show that brain / ventricle contrast is 7.9 times greater on sodium images than on proton images as reported.8 Despite of it, sodium image contrast in normal brain cells suffers from "poorly MRI visible" intracellular sodium due to its shorter T2. In the cerebrospinal fluid (CSF) and extracellular space, the sodium is adequately "MRI visible" due to much shorter T2 shown.9 The contrast in proton imaging depends upon the relaxation times as T1 and T2-contrast although these relaxation time constants do not correlate with proton concentration. We developed a approach to selectively suppress extracellular sodium using inversion recovery pulse sequence based on using optimized inversion times to generate sodium MRI contrast power to define possibly brain regional physiology and tumor physiology in animal tumor models reported.10
In terms of sodium MR measurements we need average sodium image intensity from both the single quantum and inversion recovery images, as well as single quantum image values for the sodium phantom. For the composition of intracellular sodium inversion time (T;), we need the inversion time, which produces the lowest values for the brain tumor image intensity. The
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equation for S, relating signal attenuation as a result of an inverting pulse can be given by
S = N[l - 2 exp(—TI /TJ + 2 exp(- (TR - Eq.1 TE/2)/Tl) - exp(-TR/T1)] exp(-TE/T2)
only 1/54000 of the signal obtained from the protons at the same field. This weak sodium signal was attenuated by the following approach using: short TR =100 ms; higher relaxation of spatial resolution 4 mm; observation time = 6 ms.
where N. f[T1,T2,TR,TI] is the attenuation function, which for short TE and long TR and T2, can be approximated as N. f (T1, TI). These functions give the relative dependence on the various time constants and pulse sequence intervals. The constant "N" is a function of units, equipment amplification, field strength and others instrumental parameters. Using the selected phantom just described with equation above, "N" can be determined. This equation can be used to estimate single quantum images without inversion pulses by setting the inversion time = 0. The multislice spin-echo Na MRI pulse sequence was used with repetition times on the order of 100 msecs and spatial resolutions on the order of 1 mm. During the course of a pulse cycle sodium nuclei are moved by diffusion between microscopic domains, or interchange bound to unbound. It smears the distribution of real T1 values while giving a stable composite T1, which appears well behaved in response to the inversion pulse.
Methods and Materials
MR data acquisition: In our imaging experiments a multislice spin echo (MSSE) pulse sequence was used at Hatch NMR Center, Columbia University, New York, NY as described elsewhere.1 The short echo was obtained at TE=5.6 ms. The signal from short T2 fraction of intracellular sodium was not detected and only 40% of the sodium signal arising from brain was detected due to very low "MRI visible" sodium abundance around 1/5000 than the abundance of protons. The clinical sodium images were obtained on 4.23 Tesla whole body MRI system operating at a Larmor frequency of 52 MHz (see Figure 1). Thus NMR sensitivity of sodium was 9.25% that of protons at the same magnetic field at 4.23 T. Sodium NMR signals from single echo for a given volume of brain was
Figure 1 A human 4.23 Tesla Magnetic Resonance Imaging clinical imager with wide bore of 67 cm equipped with state of art superconducting magnet.
3-D gradient echo data acquisition techniques (see Figure 2) were used for the observation of sodium at every point of the brain. For it, 3D slab gradient echo pulse was used employing inversion pulse in acquisition data matrix 64 x 64 x 24 covering the entire volume of human head to acquire 24 slices, each 1 mm thick, in 34 minutes providing additional signal averaging. In sodium brain images, gray / white matter discrimination was seen on scans showed in-plane resolution of 4 mm. Proton images were acquired by 3-D multi-spin short echo spectroscopic imaging pulse sequence at spin echo times TE= 10 and 12 ms, TR=100 ms, FOV= 90 cm, 128 x 128 matrix with total spectroscopic imaging time 46 minutes. Regional sodium concentration was estimated by a local fitting program, estimating M0= 145 mM (in vitreous humor of eye) and relative sodium values in other regions.
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Figure 2 Axial T1 weighted 23Na sodium MR human normal brain images acquired at 4.23 Tesla MRI clinical imager. Images were acquired at TR=100 ms, TE = 5.6 ms, TI= 25 ms, slice thickness = 2.5 mm, matrix 64 x 64 x 34, data points 1048. Axial brain images are shown top left towards down right for superior towards inferior slices. Coronal brain images are shown at bottom from frontal to parietal slices. At same level proton MRI images are shown in Figure 7 with approximate levels of 3 slices in reconstructed images in Figure 7 lower panels, middle row.
1	2 3 4 S 6
TQ intensity
1 2 3 4 s e
Figure 3 Phantom for comparison of SQ and TQ methods and validation was performed in 7 tubes filled (middle and right panels on top) with different concentrations of NaCl as shown in graph with their image signal intensities (Panels on bottom). For comparison, inversion recovery method was used for these phantom tubes that showed insensitive IR sodium signal to these tubes but high signal for lowest NaCl concentration (left panel on top). The signal intensities and concentrations change proportionately in both SQ and TQ methods.
Sodium concentration and MR image intensity experiments: 7 tubes filled with different increasing NaCl concentrations were arranged in a circle (see Figure 3) for projection (transaxial) imaging at TR=130 ms and resolution 100/64 mm. For Comparison and validation purposes, standard single quantum (SQ) by spin-echo at TE=9 ms, number of averages = 96, imaging time=96 x 64 x 0.13 or 13 minutes 20 seconds. For better imaging Triple quantum (TQ) images acquired by using 12 steps of refocused RF pulse in the evolution period were obtained in creation
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time =14 msec, number of averages = 1920 within four and half hours.
Figure 4 For experiments on intracellular sodium (on bottom), 2D spin echo sequence is shown as sequence diagram (A) to differentiate intracellular and extracellular sodium in a phantom model system. The phantom consists of two tubes, one is 1 M NaCl in solution and the other is 1 M NaCl in 4% agarose (Panel A). They simulate extracellular and intracellular sodium, respectively without use of inversion recovery pulse. It can be seen that an inversion time of TI = 20 ms completely suppressed intracellular sodium (Panel B) and TI = 30 ms completely suppressed the extracellular sodium signal from the tube containing NaCl in solution (Panel C). At TI = 40 ms partial suppression of extracellular sodium is highlighted (Panel D). Acquisition parameters were TR=200 msec, TE = 3.2 msec, matrix 258 x 258. For extracellular sodium and intracellular sodium images, conventional 2D-slice selective gradient echo is shown as diagram (B) and 3D-slice selective gradient echo sequence diagram (C).
Intracellular sodium MR phantom and validation: 2-D spin echo sequence was used to differentiate intracellular and extracellular sodium in a model system (see Figure 4). The phantom was made of two tubes, one had 1 M NaCl (representing extracellular sodium) in solution and the other had 1 M NaCl in 4% agarose to simulate increased viscosity and binding sites of intracellular space (representing intracellular sodium) as shown in Figure 3. These tubes
simulate extracellular and intracellular sodium images, respectively without use of inversion recovery pulse.
The extracellular sodium and intracellular sodium images were acquired with a conventional 2D-spin-echo inversion-recovery pulse sequence (IR). To validate the power of IR pulse sequence and variable inversion times (TI) to distinguish sodium populations, different TI= 10, 20, 30 ms were used. Acquisition parameters were TR=200 msec, TE=3.2 msec, matrix 258 x 258. IR pulse sequence was used to suppress contribution of sodium within a specific range centered around T1ex. Having identified this range, inversion time was set to TI=(ln 2)(T1ex), at long repetition times (TR). In these experiments, by hit and trial error, we set optimal TI=25 ms to suppress composite signal mainly from extracellular sodium. For demonstrating two different null points of intracellular and extracellular sodium populations, different "tau" values represented exponential sodium MR signals rising at different rates. It highlights the existance of two populations of sodium in MR signal with predominent extracellular sodium. Phantom studies in our system were designed for a contrast sensitivity of 0.05 mM of sodium in a disc 10 mm in diameter, for a 1-cm thick slice.
Figure 5 Na-23 MR spectrum is shown to demonstrate different null points crossing on x axis at two different "tau" values representing higher signal for extracellular sodium and lower signal for intracellular sodium. Exponential sodium signal (ln 2)(T1ex) is represented as rising IR amplitude (left figure) and their respective two different chemical shift peaks rising at different time intervals (right figure). However, intracellular sodium peak appears very small in absence of shift reagent, shown with small arrows in the right panel. In presence of shift reagent it becomes prominently larger.
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Rat brain imaging: For quality control purposes, rats were placed on horizontal plateform and small Rf coil was chosen with following dimensions: inner diameter 44 mm,outer diameter 50 mm single turn, saddle (non-quadrature type), with SNR 2.4 times better than large coil. Transaxial and coronal projection images were obtained at TR=130 msec, number of averages = 120 in 17 minutes (for SQ) and 960 minutes (for TQ) (see Figure 6).
j*
f I
Figure 6 Rat images are shown as transaxial (left) and coronal projections (right) at TR=130 ms, Number of average=120 (SQ) and 960 (TQ).
Results
Sodium weighted data acquisition time and
sensitivity of the method: A series of sodium axial and coronal T1 weighted images of the head of a normal volunteer is illustrated in Figure 5. It was difficult to discriminate CSF in the subarachnoid space from cortex in Na MR images. CSF and fluid-filled regions like ventricles and eyes showed slow T2 decay while cortex showed fast T2 measurements. The images are sequential cross-sectional representation extending from top of the head in the upper left panel to lower right panel at the level of eyes. In the first row, images showed sodium distribution almost uniform in upper parietal region. In the second row, images show the bright signal arising from CSF surrounding the brain in the subarachnoid spaces. The brain parenchyma appears darker than the surrounding CSF. The two lateral ventricles and the Sylvian fissure can be seen brighter in third and fourth row images. The suprasellar and perimesencephalic cisterns can be seen on the right images in fourth row and bottom left image.
The eyes show strong signal because of the large extracellular compartment in both vitreous and aqueous chambers. In the bottom right images, medulla oblongata rich in CSF can be seen in coronal images.
We compared different concentrations of sodium NaCl (as free extracellular sodium) for their sensitivity on SQ and TQ images. Sodium concentration and image signal intensity on SQ images showed better linear relationship over the TQ images (see Figure 3). However TQ images exhibited more sensitive for intracellular sodium (bound sodium) with short T1. The TQ image acquisition time was considerably long than the acquisition time for SQ images. So, we preferred inversion recovery method to suppress extracellular sodium signal.
Table 1 Axial human brain proton MRI and 23Na MRI imag slices were co-registered and compared by delineation of area of brain by Optimas 6.5. The area was matched and statistically analyzed in different slices
(n=14).
Brain Slices (n=14)	Area measured on Proton MRI (in mm2)*	Area measured on 23NaMRI (in mm2)**
1	0.5048	0.4702
2	0.4666	0.4404
3	0.4001	0.3528
4	0.5132	0.4559
5	0.7708	0.7729
6	0.6779	0.6641
7	0.9372	1.0358
8	0.9716	0.9456
9	0.3764	0.3732
10	0.6180	0.5924
11	0.5480	0.5216
12	0.3001	0.2959
13	0.5928	0.5683
14	0.5404	0.5539
Statistical analysis: Slope 0.9890±0.0168; 95% Confidence Intervals 0.9528 to 1.025; P value<0.0001
Comparison of proton weighted T1 and sodium weighted data acquisition time and sensitivity of the method: The reconstructed images are shown in Figure 7 for brain landmarks. Co-registration of proton and sodium image showed good
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Sharma R: Sodium Weighted Clinical Brain Magnetic Resonance Imaging
comparison as illustrated (bottom row images) for total brain cross-sectional area (r2 = 0.9830; p=0.0001) for (n=14) measurements in Table 1.
Figure 7 Contiguous brain slices are shown as axial re-construction images by rendering method (top row), parietal lobe structures in the brain in the second row (axial image, left panel) and coronal image, right panel) with matched sodium image (middle panel) represent good comparison and sodium distribution (A for eye 145 mM; B for ventricle 85 mM and C for cortex/subarachnoid space 25 mM; D for CSF 142 mM). In third row, contiguous proton axial T1 images and localized brain spectra (rightmost) are shown. As illustration for the comparison, proton T2 axial image (on left, bottom), intracellular sodium T1 axial image (on middle, bottom) and brain tumor image (on right, bottom) with tumor (T) are shown with brain area delineation by Optimas 6.5 program. For sodium, multislice spin echo IR pulse sequence, scan parameters were:TR=100 msec; TE=5.6 msec; F0V = 40 mm, slice thickness = 2.5 mm; flip angle = 90°; TI = 25 msec; acquisition matrix=64x64x34. For proton, multislice short echo IR pulse sequence was used with:TR= 200 msec;TE= 10 and 12 msec; slice thickness = 2.5 mm; matrix=128 x 128.
These measurements were comparable showing difference less than ±5% by using both methods
(Figures 7 and 8). However, the comparison may suffer from the software characteristics to delineate the brain area and measure area. For demonstration, a representative brain tumor is highlighted marked as "T" which clearly signifies the value of high sodium MR signal inytensity of tumor. In the clinical observations the signal intensity determined from the NMR images of the normal volunteers show that the lateral ventricles provide a sodium signal four times larger than that obtained from the surrounding brain parenchyma.
6 5H 4 3
□	ProtonMRI(sq cm)
□	Sodium MRI(sq cm)
0
Figure 8 Measurements of brain delineated area for six different levels are shown by proton MRI method and sodium MRI method. One representative slice image by proton MRI and sodium MRI image with delineation lines by Optimas software at same level are shown in Figure 2. Both methods showed approximately same measurements and comparison (top panel) showed insignificant statistical difference at 95% confidence limits for the data shown in Table 1.
Multislice spin echo method was specific to sodium nuclei. The sodium concentration in the different brain locations may be derived from literature on compartmental size varying 25 mM in white matter and 47 mM in cerebral cortex, which were comparable with our fitted program based
2
1
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sodium concentration mean values in white matter, cortex, CSF, superior sagittal sinus. Although several artifacts were obvious for interpreting sodium concentration in brain such as partial volume effects, centrum semiovale region, nonuniform radiofrequency field, CSF or vitreous humor as standard extracellular sodium concentration etc. Moreover, effects of inhomogeneous radiofrequency field were negligible at short echo (early echo) reported4 due to reported more brain signal at short TE (3.6 ms) than with long TE (14 ms) for MR "visible" fast T2 components of brain sodium. The standard extracellular sodium concentration (145-mM) by extrapolated M0 values may be erroneous because of multi-exponential free induction decay of sodium. However, sodium concentrations in identical regions with similar T2 decay may be good comparison such as cortex and white matter.
Optimization of inversion time in sodium phantom imaging: Using inversion recovery pulse sequence (see Apendix 1), at lower inversion time (TI) range, both free extracellular and bound intracellular sodium showed higher signal intensity decreasing in the inverse manner with rise in TI. Surprisingly, free extracellular sodium exhibited higher sodium signal intensity at the TI = 20 msec (in Figure 4), which remained high till TI < 25 msec. At TI = 30 msec, bound sodium or intracellular [Na] exhibited higher signal intensity while free sodium or extracellular sodium was not visible. These altered signal intensities may be seen on phantom images as shown in Figure 3. Sodium MR signal intensity was 4 — 6 times lower than the proton MR signal intensity in the brain as shown in Figure 2 middle panels.
Discussion
Na-23 is second most abundant MR sensitive nucleus next to proton. So, it has its magnetic resonance (MR) imaging potential. The presence of two well-defined sodium intracellular and extracellular compartments in brain provides regional information on relative size of
compartments. The first use of higher magnetic field strength at 4.23 Tesla with the use of lower noise electronics with more sensitive radiofrequency (RF) antenna provided the ease of MRI operation and better sodium MR visibility of CSF, venous spaces and cortex. On contrary, white matter is relatively sodium free. Short TR = 100 ms than that used in proton images TR = 0.2 — 2.0 sec, utilizes the advantage of relatively short T1 of sodium. Sodium fast relaxing nuclei exhibit high spatial resolution around 4 mm (for proton it is 1 mm). The observation time 4 to 6 ms for sodium was another advantage for optimal clinical evaluation best achieved by imaging the entire head. In the case of sodium imaging in particular, noise was the limiting factor. Total volume 3-D imaging approach of brain appeared the most desirable, without much to be gained by limiting the number of observed slices. In any 3 D data acquisition scheme, limiting the size of the imaging region-of-interest by reducing the number of observed planes does not result with faster NMR signal acquisition in a given imaging time and resolution. Sodium imaging seems a suitable clinical diagnostic approach using multislice spin echo Na-MRI method due to the major involvement of sodium in important dynamic processes in the cell. Free extracellular sodium concentration [Na]e depends on the metabolic energy for homeostasis and calcium, hydrogen ions through coupled exchangers in the brain.
From biomedical standpoint of sodium signal source in brain, each cell gets metabolic energy through the biosignal processing so called "transmembrane active potential" of cell across sodium-potassium ion channels. These channels do function at the expense of metabolic energy ATP controlling the sodium and potassium transport known as "sodium pump" and biochemical phenomenon is known as "Na+/K+ ATPase enzyme system". High concentrations of extracellular sodium protect loss of intracellular sodium naturally. However, in abnormal situations in the cell tend to force the ATP metabolic energy to activate ion-channels to allow only limited amounts of intracellular sodium to leave the cell and cross the membrane. This type of activated
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process causes "activation potential" generation and drop in cell membrane electrical activity or threshold membrane voltage to maintain to Na+/K+ ion balance overall in side the cell. Presumably, this small subphysiological energy exchange during intracellular sodium transport provides the opportunity to generate the magnetic resonance effect as visible spin echoes at different quantum levels such as single-, double-, triple-, multiple quantum filters. Henceforth, this property generates sodium as MR visible by using different pulse sequences based on sodium quantum levels.
We have used in our lab these different pulse sequences such as single-, double-, triple- and multiple quantum filter methods as reported.2,6-8 Other recent fast 3D sodium brain imaging studies were reported using 2.0 MR clinical imager11 based on fast T(2)(*) relaxation component between 1.2-1.6 ms and slow component T(2)(*) between 7.1-8.4 ms. Alternative approaches for sodium quantification using regional T2 values and sodium measurements by extrapolated equilibrium magnetization (Mo) and multiple short echo pulses using short T2 (0.7-0.8 ms) and long T2 (7.0-26.0 ms) are still in infancy. However, sodium-imaging studies are limited. Recently double tuned sodium coils were reported for apparent diffusion coefficients (ADC) imaging in focal cerebral ischemia to highlight accumulation of sodium in
dead tissue.12
To our knowledge, intracellular sodium MR properties seem to be very dependent upon selection of scan parameters such as variable inversion times (TI) and (TE) as shown in Figure 3. Still, there is trade-off between high magnetic field with imaging long acquisition time and achievement of absolute intracellular sodium signal by suppressing majority of extracellular sodium signal. Henceforth, the sole approach of the inversion recovery effect without use of any contrast agent injection appears valuable and needs shorter image acquision time than its counterpart TQ method. This fact of inversion recovery effect on high intracellular sodium rich fluid containing parts of the brain and phantom can be observed (see Figures 3 and 4) where in
Figure 3 intracellular phantoms appear brighter and extracellular or free sodium signal appear completely suppressed. This fact further highlights the necessity of inversion recovery effect due to its longer rise time of longitudinal magnetization for T1 and superiority of inversion recovery pulse sequence. Nevertheless, complete suppression of extracellular sodium MR signal is not possible in tissue imaging because of the fast dynamicity of sodium in the brain and cells. This fact is demonstrated in rat images where transaxial images showed poor SNR. Relatively, use of higher magnetic resonance fields may enhance the selective sodium MR visibility in future to answer pathophysiological conditions of brain.
Sodium is altered in many other pathophysiological conditions of clinical interest; and hence, is an indicator for many types of pathology including colon, uterine malignancy, arrhythmia and stroke as reported.2 Recently, emphasis was shifted to study the cause of increased intracellular sodium in neoplasia vs normal tissue, malignant vs benign tumors and poorly differentiated vs. well-differentiated tumors as reported.13 Antineoplastics can change cell cycle distribution often leading to apoptosis as a result, both changes in cell cycle phase and apoptosis alter intracellular sodium [Na];.14 For this intracellular sodium [Na] change, due to its multiple spin state transitions, it needs be examined through specific pulse sequences where selectively molecular target imaging in cancer tumors, may answer enhanced antineoplastic drug cytotoxicity e.g. gene expression imaging. Higher ratios of intracellular Na+/K+ in both benign and malignant tumors over their normal cellular counterparts were recently reported.15 Increased intracellular sodium [Na] have been well-described fact in a variety of biological systems during normal and pathophysiological events relevant to chemotherapy including movement throughout the cell cycle, apoptosis, necrosis, metabolic suppression, and transformation from normal to neoplastic tissue. [Na] changes occur within minutes or hours in response to transmembrane flux alterations or subcellular sequestration.
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Whereas proton MRI, which uses the hydrogen nucleus, is ideal for morphological tumor studies, while Na-MRI seems ideal for sub-physiological imaging due to its major involvement of Na in cellular dynamic processes and sodium interaction with chemotherapy, cellular apoptosis and ions. We attempted proton spectroscopic imaging of brain and we could get in vivo information of different metabolites as shown in Figure 2 (middle panels). High resolution of this metabolite information encouraged us to look over sodium spectroscopic imaging possibility. This possibility now seems very feeble due to very weak sodium signal without use of shift reagent. Alternatively, use of inversion recovery pulse appeared unique power to visualize intracellular sodium. With advanced software and magnetic resonance methods, we presume it would be possible to get sodium spectroscopic images. Other main advantage of this sodium MR imaging method is no use of shift reagents to enhance contrast-noise-ratio (CNR). To establish the sodium MR signal and sodium images of brain, sodium images could be compared with well MR visible proton images. However, the comparison may suffer from the software characteristics to delineate the brain area and measure the brain area. Results show statistical agreement in measurements by both methods.
Superiority of sodium MR imaging method over other in vivo methods can be attributed due to its capability of extracting out sodium ion changes in membranes actively participating in drug induced sodium transport alterations, screening the drug induced effect on molecular targeting in cancer tissue cells as reported by Sharma et al., 2001. In near future, several cellular targets may be imaged possibly altered by sodium ion activity such as MR active ligands bound with sodium-linked gene, Na+/K+ ATPase, reflecting molecular targets of neoplasia, destruction of actin filaments, microtubules, gene expression and suppression of protein synthesis and metabolism.
Conclusion
Sodium human brain images were acquired by rapid, in vivo sodium-23 magnetic resonance imaging using non-invasive multiple quantum filtered multislice spin echo pulse method. Intracellular sodium and extracellular sodium phantoms demonstrated the power of multiple quantum filter to suppress either extracellular or intracellular sodium population at different inversion times. Sodium images showed comparable contrast with respective proton images with diagnostic accuracy. The method may enable the interpretation of borderline malignancy, histologically difficult tumor composition and drug monitoring in clinical set up. Still it is very early to predict the power of sodium MRI in clinical utility and needs further investigations and validation.
Acknowledgements
Author acknowledges Dr. Ed X.Wu, Ph.D. and Dr.JK Katz at Columbia University, New York and Professor R.K.Gupta at Albert Einstein College of Medicine, New York for their laboratory facilities.
Literature
1.	Hilal, SK, Moudsley AA, Ra JB et al. In vivo NMR imaging of sodium-23 in the human head. J. Computer Assisted Tomography 1985, vol 9, 1-7.
2.	Dizon, J M, Tauskela JS, Wise D, Burkoff D, Cannon PJ, Katz. Evaluation of Triple-quantum-filtered 23Na NMR in monitoring of intracellular Na content in the perfused heart: Comparison of intra- and extracellular transverse relaxation and spectral amplitudes. Magn. Reson. Med. 1996, Vol 35, 336-345.
3.	Permann WH, Turski P, Houston L.,Glover GH, Hayes CE Methodology of in vivo human sodium NMR imaging at 1.5 Tesla. Radiology 1986, vol 160, 811-820.
4.	Ra JB, Hilal SK, Cho ZH A method for in vivo MR imaging of the short T2 component of sodium-23 Magn Reson Med. 1986, vol 3, 296-302.
5.	Ra JB, Hilal SK, Oh CH An algorithm for MR imaging of the short T2 fraction of sodium using the FID signal J.Comput Assist Tomogr 1989, vol 13, 302-309.
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Sharma R: Sodium Weighted Clinical Brain Magnetic Resonance Imaging
6.	Jung K J, Katz, J. Chemical-shift-selective acquisition of multiple —quantum filtered 23-Na signal J Magn. Reson. 1996 vol 112, 214-227.
7.	Jung KJ, Katz J, Boxt LM, Hilal SK, Cho ZH. Breakthrough of single-quantum coherence and its elimination in double-quantum filtering.
J.Magn.Reson. 1995, vol 107, 235-241.
8.	Hancu I, Boada FE, Shen GX Three-dimensional triple quantum-filtered 23-Na imaging of in vivo human brain. Magn.Reson.Med. 1999, vol 42, 1146-1154.
9.	Winkler SS, Thomasson DM, Sherwood K., Permann W Regional T2 and sodium concentration estimates in the normal human brain by sodium-23 MR imaging at 1.5 T J. Comput Assist Tomogr 1989, vol 13(4), 561-566.
10.	Sharma R, Katz J. Quantitative validation of in vivo intracellular sodium signal acquisition at 4.23 T MRI by applying inversion recovery pulse: First intracellular sodium-cell S phase correlation. ENC conference W &TH P#155, 2001.
11.	Kohlar S, Preibisch C, Nittka M, Haase A. Fast three-dimensional sodium imaging of human brain.
MAGMA 2001, vol 13(2), 63-69.
12.	Lin SP, Song SK, Miller JP, Ackerman JJ, Neil JJ. Direct, longitudinal comparison of (1)H and
(23)Na MRI after transient focal cerebral ischemia. Stroke 2001, vol 32(4), 925-932.
13.	Kline R, Wu EX, Petrylak DP, Szabolcs M, Alderson PO, Weisfeldt ML, Cannon PJ, Katz J. Rapid in vivo monitoring of chemotherapeutic response using weighted sodium magnetic resonance imaging. Clin Cancer Res. 2000, vol 6(6), 2146-56.
14.	Sharma R, Katz J. Minimization of data acquisition in intracellular sodium [Na] weighted microimaging using inversion pulse sequence at 4.23 Tesla MRI to correlate increased [Na] in apoptosis rich tumors. Proceedings of ISMRM Workshop on "Data Minimization: More Outcome with less" Marco, Florida, 18-21 0ct.2001, pp 6872.
15.	Sharma R, Wu EX, Kline R, Szabolcs M, Cannon PJ, Katz J Rapid in vivo monitoring of methyl-nitroso-urea(MNU) induced breast tumor response to taxotere in rats using intracellular sodium 4.2 Tesla magnetic resonance imaging and immunohistological characterization. Abstract #230 in Proceedings of AACR-NCI-EORTC International Conference at Miami, Florida Oct.27-Nov. 2,2001.
Appendix 1: Inversion Recovery Pulse Sequence source code
Rakesh.PPL
Author: Rakesh Sharma
Date:	August 11, 1999
Version:	1.0 based on Rakesh.ppl
#use RF1 "c:\smis\seqlib\latch31.seq" latch
#use RF1 "c:\smis\seqlib\rfshapes.seq" pf1
#use GRAD "c:\smis\seqlib\gr3040_c.seq" grad
\\ Slice separation is centre to centre NOT slice gap.
Re PARAMLIST
OBSERVE_FREQUENCY "1H", -20000, 20000, 0, MHz, kHz, Hz, rx1MHz; EDITTEXT "Receive Frequency ","Hz ","%i",-32767,32767,0,1,rec_freq; SPECTRAL_WIDTH 1000, 400000, 25000, sample_period; SCROLLBAR "Phase gradient ","offRon","%u",0,1,1,1,gp_on; NO_SAMPLES 64, 512, 256, no_samples; DISCARD 0, 10, 0, no_discard;
SCROLLBAR "Oversample? ","(Yes = 1)","%d",0,1,0,1,oversample; NO_VIEWS 1, 512, 48, no_views; NO_AVERAGES 1, 30000, 2, no_averages; PHASE_CYCLE 1, 4, 2, phase_cycle;
EDITTEXT "My-phase: 0=off, 2=dc, 4=Cyc","90 deg","%i",0,4,2,1,my_phase; EDITTEXT "No. of dummy scan ","1 scan","%i",1,16,4,1,no_dummy;
EDITTEXT "Patient X angle", "degrees", "%.1f", -1800, 1800, 0, 10, subj_angle_x;
EDITTEXT "Patient Y angle", "degrees", "%.1f", -900, 900, 0, 10, subj_angle_y;
EDITTEXT "Patient Z angle", "degrees", "%.1f", -1800, 1800, 0, 10, subj_angle_z;
GRADIENT_STRENGTH grad_var;
SLICE_THICKNESS 32767, 1500, gs_var;
SLICE_SEPARATION -6000, 6000, 1500, slice_offset;
NO_SLICES 1, 128, 1, no_slices;
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SLICE_INTERLEAVE 1, 5, 2, slice_interleave; FOV 32767,32767,2.0e-3,gr_var,gp_init_var;
MULTI_ORIENTATION 0, 3, 0, phase_var, r_angle_var, p_angle_var, s_angle_var; FOV_SLICE_OFF -12000, 12000, 0, 400, fov_slice_off; FOV_PHASE_OFF -32000, 32000, 0, 4000, fov_phase_off; FOV_READ_OFF -32000, 32000, 0, 4000, fov_read_off ;
EDITTEXT "Ramp time (multiple of 5 only)","us","%d",100,2000,500,1,tramp;
EDITTEXT "RF Pulse Shape = ","(1-6)","%d",1,6,6,1,rfnum;
SCROLLBAR "Obs Mod, power level ","(0 =off)","%u",0,4,1,1,obs_mod_level;
SCROLLBAR "RF Calibration value ","%","%.2f",0,2047,256,20.48,rfcal;
SCROLLBAR "Scale for length of hard rf ","%","%d",50,150,100,1,scale_hd;
SCROLLBAR "Scale for IR 180 rf amplitude ","%","%d",0,200,100,1,scale_ir;
EDITTEXT "Repetition time","ms","%d",1,2000,300,1,tr;
EDITTEXT " Inversion time","ms","%d",1,10000,10,1,ti ;
EDITTEXT "Echo time","ms","%d",1,500,30,1,ta;
EDITTEXT "180 FID crusher gradient","us","%d",10,10000,1000,1,tcrush; SCROLLBAR "Report Min TE and Tr times? ","(Yes = 1)","0/od",0,1,0,1,report_on; EDITTEXT "GR: spoil after acq, ","us","%d",0,5000,1000,1,tf ilter;
SCROLLBAR "Read gradient compensation ","DAC UNITS","%d",-500,500,0,1,gr_comp_scale;
SCROLLBAR "Slice grad. comp, time ","usec","%d",-500,500,0,1,t_gs_comp;
SCROLLBAR "Slice gradient " ,"off/on","%u",0,1,1,1,gs_on;
SCROLLBAR "Read gradient ","off/on","%u",0,1,1,1,gr_on;
DSP_ROUTINE "c:\smis\dsp";
DATA_TYPE 0x13;
END */
/* Single/multi-angle oblique switch #define MAO #define MAX_SLICE 128 #include "stdfuncs.pph" #include "stdconvs.pph" #include "variabl2.pph" #include "off_mao.pph" #include "mac3040c.pph" #include "mac3031c.pph" void	setsync(int);
long	gp_ inc_ long ;
int	no_rf_lobe ,thd180 ,p IR_mu l ,my_phase ;
int	no_dummy ,no_ds ;
int	ti_ms ,ti_us ;
common int scale_hd, scale_ir,ti;
do not define this for Single
int RS {
_phase(int nrf, int navg, int ncycle)
int istep,	angle;
int p1[6]	= {0,2,0,3,2,1}
int p2[6]	= {0,0,0,0,0,0}
int p3 [6]	= {0 ,2 ,0 ,1 ,2 ,3} istep = navg % ncycle;
if (ncycle ==	4) istep = istep ■
if (nrf == 1)	angle = p1[istep
if (nrf == 2)	angle = p2[istep
if (nrf == 3)	angle = p3[istep return angle;
first 2 steps for DC_correction second 4 steps for Cyclops
Cyclops rf 90 rf 180 receiver
main(){
/* no_discard = 0; */ view_block = no_averages; slice_block = 1; asymm = 0;
# inc lude "testexp .pph "
/* The arguments after each frame name are the pulse length (in usecs) and the pulse width (in Hz) which are written to the arrays 'rf_length' and 'rf_bwdth' respectively. */
NEWSHAPE_MAC(1 NEWSHAPE_MAC(2
newshape_mac(3 newshape_mac(4
NEWSHAPE_MAC(5 NEWSHAPE_MAC(6
pf pf pf pf pf pf
3lobe_sinc_6kHz",
3lobe_sinc_3kHz",
3lobe_sinc_1500Hz'
5lobe_sinc_6KHz",
5lobe_sinc_3kHz",
5lobe sinc 1500Hz'
666, 6000) 1332, 3000) , 2664, 1500) 1000, 6000) 2000, 3000) , 4000, 1500)
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The phase resolution. */ phase_ increment(1) ; /* discard(no_discard); */ d iscard (0) ;
The gradient strengths are negative: tacq = sample_period
tacq = (tacq * tacq_2 = tacq tref = tacq_2 if (rfnum < 4) else templ1 tsel90 templ2 templ3 thd180
'10L
gs_var, gr_var and gp_init_var. \\ long integer \\ acquire time in us. \\ long integer
1)
no_samples) / 2;
(tramp/2); no_rf_lobe = no_rf_lobe = rf_length[rfnum]; templ1;
templ1 / (no_rf_lobe + scale_hd * templ2; templ3 / 50L; pulse_bwdth = rf_bwdth[rfnum]; gr_oversample = oversample + 1; /* Scale factors defined in the PARAMLIST header (a factor of ten). */ scale_read_off = 4000; scale_slice_off = 400; scale_phase_off = 4000; /* RF deg_90. */
deg_90 = scale(90,1000, phase_res); /* if phase_res =225 then deg_90 will = 400 */ /* Calculate the phase encode increment(gp_inc). gp_init_var is the initial value passed over from PARSETUP based on an effective phase encode time of 2 ms. */ templ1 = gp_init_var; templ1 = templ1*2000L; templ1 = templ1/(tref + tramp); gp_init_var_rescale = templ1; gp_inc = scale(templ1, 2,no_views); gp_inc_long = scale(templ1, 2,no_views); ret = dacmaxlong + templ1; if (ret < 0 ){
printf("'FOV too small in phase encode direction"); goto end;
}
Calculate Gs. templ1 templ1 templ1
gs_var;
templ1*pulse_bwdth; templ1/1500L; gs_var_rescale = templ1; ret = dacmaxlong + templ1; if (ret < 0 ){
printf("' Slice thickness too small for pulse selected "); goto end;
}
GR_COMP is the amplitude of the read preparation pulse. */ templ3 = gr_var / gr_oversample; gr_comp = templ3; templ1 = tacq_2 + (tramp/2); templ2 = tref + tramp; if (templ1 > dacmaxlong ) {
templ3 = (templ3 * templ1)/ templ2;
}
else templ3 = scale( templ3, templ1,templ2); ret = dacmaxlong + templ3; if (ret < 0 ) {
printf("' FOV too small for read preparation pulse "'); goto end;
}
Macro call to avoid initial glitch from 3031 board. */
MR3031_DEGLITCH Macro call to ouput zeros to 3040 board. */
BASEMATRIX( subj_angle_z, subj_angle_y, subj_angle_x ) /* Calcluate Base matrix. delay(caldelay,us); /* Matrix calculation delay of 100 us. */ /* 3040 deglitch call must be made after base matrix call. */ MR3040 DEGLITCH
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Reset receiver phase to zero 100 us
Synchronise to 100kHz.*/
\\ 1 = dummy, 0 = acquire
\\ Reset phase encode value for next image. \\ Counter for P.E. (views) loop.
\\ Counter for view block loop. \\ Counter for multislice loop.
1
/* 100 usec is the min tramp which sets the clock to 20 ie 20*100ns = 2usec. This is the chosen minimum setting of the clock. */ clock = tramp/5; MR3040_C lock (clock) ; /* Create gradient lists. */
MR3040_SetL istAddress (0) ; slice_list = MR3040_InitList(); POSPULSE_HOLD ()
read_list = MR3040_InitList(); POSPULSE_HOLD ()
phase_list = MR3040_InitList(); POSPULSE_HOLD () phase_rec = 0; setsync (2000) ; sync(); starttimer () ;
MR3031_setup (latch , "rf_off" ,0 ,0 ,0 ,0) MR3031_go(); de lay (20 ,us) ; /*******MAIN ACQUISITION LOOP ******/ image_av = 0; no_ds = no_dummy; dummy_cycles(no_ds); averages_loop:
gp_var=gp_init_var_rescale; V current_view = 0;	V
phase_encode_loop:
current_view = current_view + 1 view_av = 0;	V
view_block_loop:
current_slice=0; multislice_loop:
current_slice = current_slice slice_av=0;	\\ Counter for slice block loop,
if (no_ds < 1) no_acq = view_av + image_av;
\\ No. of acquisit. of a given segment of data (used to control phase cycling). /* Calculate the offsets in terms of frequency and phase increments. */ SELECTSLICE( current_slice )
READOFF(fov_read_off, sample_period, scale_read_off,fov_read_freq) PHASEOFF(fov_phase_off, phase_res, scale_phase_off, fov_phase_deg) SLICEOFF(fov_slice_off, pulse_bwdth, scale_slice_off, fov_slice_freq) /* RF RECEIVE PHASE INCREMENT TO ACHIEVE OFFSET IN PHASE ENCODE DIRECTION, */ templ1 = IntToLong(current_view) * IntToLong(fov_phase_deg); phase_rec_cal2 = scale(360,1000,phase_res); phase_rec = templ1%phase_rec_cal2;
if (gp_on == 0 ) phase_rec =0; \\ If profile required, then do not change receiver phase /* Set up frequency offsets for current slice. */ frequency_buffer(0); frequency(MHz, kHz, Hz, rx1MHz); offset_frequency(fov_slice_freq);
slice_block_loop:
/* RF calibration value. */
templ1 = rf_length[rfnum]; tsel90 = templ1;
templ2 = templ1 / (no_rf_lobe + 1); templ3 = scale_hd * templ2; thd180 = templ3 / 50L; p90_mul = rfcal; p180_mul = 2 * rfcal; templ1 = p90_mul * scale_IR; templ2 = templ1 / 100L; p I R_mu l = templ2; /* Calculate delays for accurate echo time. */
t_refocus = ((tsel90 - tramp)/2) + tcrush + t_gs_comp; t_refocus_2 = t_refocus/2; /* Check that the t_gs_comp selected. */ if (t_refocus_2 < 4) {
printf(" t_gs_comp is too negative. \n");
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Sharma R: Sodium Weighted Clinical Brain Magnetic Resonance Imaging
goto end ;
}
/* Now calculate make up time to ti and put result in ti_ms, ti_us. */ temp l1 = (thd180/2) + tramp + tsel90 + (tsel90/2); temp l3 = ti;
temp l3 = (templ3*500L) - templ1; ti_ms = temp l3/1000L ; temp l1 = 1000*ti_ms ; ti_us = templ3 - templ1; if (templ3 < 0 ) {
US_TO_MS (templ3*2 , ret)
printf(" TI is too short by %d ms\n", - ret); goto end;
}
/* Now calculate make up time to te and put result in te_ms, te_us. */
temp l1 = (tsel90/2) + tcrush + (3* tramp) + tref + (tramp + thd180/2); temp l3 = te;
temp l3 = (templ3*500L) - templ1; te_ms = temp l3/1000L ; temp l1 = 1000*te_ms; te_us = templ3 - templ1; if (templ3 < 0 ) {
US_TO_MS (templ3*2 , ret)
printf(" TE is too short by %d ms\n", - 2*ret); goto end;
}
/* 3 ms to allow for matrix cals and other overhreads in tr_min calculation. Extra 1 ms used in printf statement to round up to nearest ms value. */
tr_min = te*1000L + tacq_2 + tfilter + tramp + (tramp + tref + thd180/2) + 3000L; tr_min = tr_min + tramp; \\extra delay at pre-90 tr_extend = ((tr*1000L/no_slices) - tr_min)/1000L; if ( tr_extend < 0 ) {
printf(" TR too short extend by %d ms\n
(((tr_min-(tr*1000L/no_slices))*no_slices/1000L) + 1)); goto end;
}
/* if (report_on == 1) printf(" Min TR = %d ms \n", (tr_min*no_slices/1000L) +1); */ /* Calculations supporting real-time adjustments. Check for overflow. */
MR3040_SelectMatrix( calc_mat ); /* Select unused matrix during calcs */
templ3 = gr_comp + gr_comp_scale;
gr_comp_s = templ3;
if (templ3 + dacmaxlong < 0) {
printf ("Reduce Read compensation\n ") ; goto end;
}
/* Calculate the matrices for this phase-encode step. Only the phase-encoding and refocus matrices needs to be rescaled, but recalculate all matrices for real time updates of angulations. */ CREATE_MATRIX( ss_mat, gs_on*gs_var_rescale, 0,0) delay( caldelay, us );
CREATE_MATRIX(aq_mat, 0, 0, gr_on*gr_var/gr_oversample) delay( caldelay, us );
CREATE_MATRIX(pe_mat, 0, gp_on*gp_var, gr_on*gr_comp_s) delay( caldelay, us);
/* Set up receive frequency buffer and select the frequency for the current slice.*/ frequency_buffer (1) ; frequency(MHz, kHz, Hz, rx1MHz); offset_frequency(rec_freq + fov_read_freq); frequency_buffer (0) ; reset_frequency () ;
/* Set phase according to PHASE_CYCLE keyword and acquisition number. */ ret = 0;
if (my_phase < 1) {
phase_90 = aqphase(no_acq,phase_cycle) * deg_90; phase_180 = 0 ;
}
else {
phase_90 = KJ_phase(1,view_av,my_phase) * deg_90;
phase_180 = KJ_phase(2,view_av,my_phase) * deg_90;
if (phase_cycle < 2) ret = KJ_phase(3,view_av,my_phase) * deg_90;
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}
if (report_on > 0) printf(" RF phases = %d %d \n phase_90, ret); ret = ret + phase_rec;
rphase(ret);	\\ Increment rec phase for offset in pe dirn.
/* Waittimer function used to fix a time of 3ms for overhaed time of create matrix calculations and loop structures etc. */
/* if (report_on == 1) printf("te1,te2= %d %d %d %dn,te_ms,te_us,te_ms_2,te_us_2); */ waittimer( 30000 ); time = hostrequest(); resync () ;
/********** SEQUENCE STARTS HERE **********/
MR3040_SetList( read_list, CHANNEL_R ); MR3040_SetList( phase_list, CHANNEL_P ); MR3040_SetList( slice_list, CHANNEL_S ); MR3040_SelectMatrix( ss_mat );
MR3040_Clock(clock);	\\ Extra 3040 clock call for 3040 synchronization,
phase (0) ; resync () ;
if (scale_ir >0) {
MR3031_HARDPULSE(tramp,thd180,pIR_mul, obs_mod_level);	\\ inversion rf
}
else {
ret = thd180; de lay (ret ,us) ;
}
if( ti_ms > 0) delay( ti_ms,ms);
if( ti_us > 4) delay( ti_us,us);
MR3040_Start(CHANNEL_S) ;
starttimer () ;
ret = tramp * 10;
phase (phase_90) ;
wa ittimer (ret) ;
ret = tsel90;
de lay (ret ,us) ;
MR3031_RFSTART(rfnum,pf1,tsel90,p90_mul,tramp, obs_mod_level);	\\ 90 rf
delay(tcrush ,us) ; MR3040_CONTINUE(CHANNEL_S) ; delay (tramp ,us) ; /* Start phase-encoding and read diphase. */ MR3040_SelectMatrix( pe_mat ); MR3040_Start( CHANNEL_R|CHANNEL_P) ; delay(tramp, us); de lay (tref ,us) ;
MR3040_Continue( CHANNEL_R|CHANNEL_P) ;
starttimer () ;
ret = tramp * 10;
phase (phase_180) ;
wa ittimer (ret) ;
MR3040_SelectMatrix( ss_mat ); MR3040_SetList( slice_list, CHANNEL_S ); MR3040_SetList( read_list, CHANNEL_R ); if( te_ms > 0) delay( te_ms,ms); if( te_us > 4) delay( te_us,us);
MR3031_HARDPULSE(tramp,thd180,p90_mul, obs_mod_level);	\\ 180 rf
/*
ret = tramp - warmup; de lay (ret ,us) ; ret = thd180 * 10;
MR3031_setup(latch,nrf_analog_onn,0,0,0,p90_mul); rfampon(0); MR3031_go(); delay (warmup.us); rfpulse(ret, obs_mod_level); MR3031_setup(latch,nrf_offn,0,0,0,0); MR3031_go(); */
delay (tramp ,us) ; MR3040_Start(CHANNEL_S) ; delay (tramp ,us) ;
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Sharma R: Sodium Weighted Clinical Brain Magnetic Resonance Imaging
de lay (t_refocus , us) ; MR3040_CONTINUE(CHANNEL_S) ; delay (tramp ,us) ;
if( te_ms_2 > 0) delay( te_ms_2,ms); if( te_us_2 > 4) delay( te_us_2,us); starttimer () ; ret = tramp * 10; MR3040_SelectMatrix( aq_mat ); MR3040_Start(CHANNEL_R) ; frequency_buffer (1) ; reset_frequency () ; wa ittimer (ret) ; resync () ;
acquire( sample_period, no_samples );
de lay (tf ilter ,us) ;	\\ GR at post-acqu is it ion
MR3040_Continue (CHANNEL_R) ; delay (tramp ,us) ;
/* If the time for the execution of hostrequest is > 10 then pause in SCAN has probably been selected. So, reset 'time' to zero. */
if (time > 10 ) time = 0; /* Now calculate the time to make up to the required 'tr-extend'. */ tr_extend = (tr/no_slices) - tr_min/1000L - time; if ( tr_extend > 0 ) delay(tr_extend,ms);
starttimer();	\\ time overheads ie matrix calculations etc
no_acq = no_acq + 1;	\\ acquisition number (for phase cycling)
slice_av = slice_av + 1;
if( slice_av < slice_block )	\\ Slice block
goto s lice_b lock_loop ; if (current_slice < no_slices)	\\ Multislicing
goto mu ltis lice_loop ; if (no_ds > 0) {	\\ dummy scans
no_ds = no_ds - 1;
if (no_ds == 0) dummy_cycles(no_ds); \\ start to acquire goto v iew_b lock_ loop ;
}
view_av = view_av + slice_block;
if (view_av < view_block)	\\ View block
goto v iew_b lock_ loop ; gp_var = gp_init_var_rescale - (current_view * gp_inc_long); /* gp_var = gp_var - gp_inc; */ if (gp_var < -dacmaxlong) {
printf(" Overflow on GP %d DAC \n ", gp_var); goto end;
}
if (current_view < no_views)	\\ Phase encode
goto phase_encode_loop ; noop;	/* THIS IS NECESSARY AT THE MOMENT AS THEER IS A BUG WITH THE COMPILER */
image_av = image_av + view_block;
if (image_av < no_averages)	\\ Averages
goto averages_loop ;
end: }
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Strokovno-znanstveni prispevek ■
Primerjava 2-D in 3-D slik vidnega polja v načinu "threshold" z diferenčno metodo
Andrej Ikica, Uroš Prelesnik, Branko Ikica
Izvleček. V članku opisujemo metode za obdelavo podatkov o vidnem polju pacienta, ki jih vrača naprava HFA (Humphrey Field Analyser). Ta naprava deluje v dveh načinih: "threshold" in "screening". V članku opisujemo "threshold" način preiskave pacienta, kjer nam naprava vrača različne vrednosti vidnega polja v decibelih. Te podatke zajamemo, jih obdelamo ter prikažemo v dvo- ali tri-dimenzionalnem prostoru. Za zdravnika je ključnega pomena primerjava posameznih vidnih polj pacienta, saj lahko na podlagi le-te postavi pravilno diagnozo (glavkom, tumor...)1,2. Razvili smo dve metodi primerjave vidnih polj. Prva temelji na primerjavi poljubnih dveh slik vidnega polja, kjer v vsaki točki izračunamo medsebojno razliko vrednosti. Druga metoda temelji na primerjavi vidnega polja z optimalno ploskvijo. Ker se le-ta s starostjo spreminja, jo je potrebno prilagajati glede na starost pacienta.
Comparing 2-D and 3-D campus images in threshold mode using difference method
Institucija avtorjev: Iks d.o.o., Trbovlje (AI, BI) in ZD Trbovlje (UP).
Kontaktna oseba: Andrej Ikica, Iks d.o.o., Obrtniška cesta 0N, 1420 Trbovlje. email: andrej.ikica@iks-doo.com.
Abstract. In this article we present methods for processing campus data of a patient. The data is received from HFA (Humphrey Field Analyser). HFA works in two modes: threshold and screening mode. We focused our work on threshold mode, where HFA provides numerical values (intensities) of campus. We capture HFA data, process it and display it in 2-D or 3-D space. Possibility to compare different campuses of patients is essential to medical staff. It is a powerful tool for diagnostics. We developed two different methods for comparing campuses. First is based on comparison of two different images of campus, where difference is computed for every pixel in image. Second method compares image of a campus with an "optimal" surface of campus, campus of an average patient. Optimal surface varies with patient's age, so we have to modify it in a regular way.
■ Infor Med Slov: 2005; 10(1): 73-78
74 Ikica A et al.: Primerjava 2-D in 3-D slik vidnega polja v načinu "threshold" z diferenčno metodo
Uvod
V okulistični ordinaciji Zdravstvenega doma Trbovlje je nameščena naprava Humphrey Field Analyser (v nadaljevanju HFA). Naprava deluje v načinu "threshold" (v nadaljevanju prag) na naslednji način: v času posameznega pregleda, ki traja približno 20 min., naprava naključno prižiga eno od točk, ki so razporejene v obsegu vidnega polja pacienta (slika 1). V primeru, da pacient lučko vidi, pritisne gumb. V nasprotnem primeru naprava smatra dogodek kot zgrešitev. Po opravljenem pregledu naprava izračuna število zadetkov v posameznem delčku komore. Rezultat je podan v decibelih.
Ilustrirajmo povedano na primeru. Recimo, da je naprava na določenem delčku komore prižgala luč 10-krat. Če jo je pacient videl 7-krat, naprava to vrednost statistično obdela in za to mesto mreže vrne vrednost, ki predstavlja jakost zadetka.
Na napravi HFA že teče tovarniški program, ki vizualno predstavi vidno polje, vendar so se v ZD Trbovlje odločili za izgradnjo lastnega programa, ki bo odpravljal pomanjkljivosti omenjenega programa:
•	nezdružljivost podatkovne baze zdravstvene ustanove in naprave HFA (neujemanje šifer pacientov),
•	nefleksibilnost programskega dela HFA.
Omenjene slabosti smo z razvojem novega programa odpravili.
Zajem podatkov
Zajem podatkov iz naprave HFA poteka preko vrat RS-232C. Podatki o vidnem polju, ki jih dobimo v osebni računalnik, so v obliki matrike 10x10, vsak element le-te pa predstavlja jakost v decibelih.
Vizualno si podatke lahko predstavljamo v obliki mreže na sliki 1.3
Slika 1 Točke predstavljajo jakost posameznega dela vidnega polja.
Predstavitev podatkov
Podatke vidnega polja prikažemo tako, da vsaki točki mreže (x,y) priredimo koordinato z, ki predstavlja skalirano vrednost vidnega polja. Podoben prikaz se uporablja za upodobitev višine terena na zemljevidih.
27	31
2B
32	29	33
Slika 2 Triangulacija (z rdečo so označene interpolirane točke).
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Za učinkovit prikaz vidnega polja (predvsem v 3D) smo morali opisani postopek nekoliko modificirati: matriko vrednosti vidnega polja velikosti 10x10 smo s pomočjo triangulacije razširili na matriko 50x50. Postopek triangulacije je preprost in ga prikazuje slika 2.
Pri postopku triangulacije interpoliramo zgornjo (zgornji trikotnik) ter spodnjo (spodnji trikotnik) ravnino, ki ju napenjajo 4 sosedne točke. Iz enačbe ravnine dobimo med sosednima točkama v vsaki dimenziji 4 nove točke. Opisani postopek nam da gostejšo matriko (50x50), ki se je izkazala za učinkovito pri prikazu podatkov, zlasti v 3-D prikazu so zaradi tega ploskve bolj gladke.
2-D predstavitev podatkov
Generirana matrika velikosti 50x50 je izhodišče za
2-D	kot tudi za 3-D prikaz. V primeru 2-D prikaza ^-vrednosti rangiramo v razrede 0-5, 5-10 itd. Vsak razred predstavimo z določenim odtenkom sive barve. Tako je razred z nižjimi vrednostmi bolj temne, razred z višjimi vrednostmi pa bolj svetle barve. Primer 2-D prikaza je na sliki 1.
Zaradi lažjega pregleda in razumevanja slike vidnega polja ima uporabnik programa možnost pregleda vrednosti osnovne matrike velikosti 10x10, ki jih vrne aparat in niso interpolirane. Vrednosti so vidne na sliki 1 in so označene z rdečo barvo.
3-D	predstavitev podatkov
Pri prikazu vidnega polja v 3-D smo postopali podobno kot v primeru 2-D. Namesto z barvo smo v omenjenem primeru razred okarakterizirali z višino določene točke. Za prikaz v 3-D (slika 3) smo uporabili tehnologijo OpenGL.
Uporabniku so v 3-D načinu na voljo funkcije za lažji pregled modela vidnega polja. Te so: drsniki za rotacijo slike v vseh treh dimenzijah, povečava ter pomanjšanje slike (zoom)...
Primerjave vidnih polj
Ključnega pomena za zdravnika pri analizi vidnega polja pacienta je možnost primerjave vidnih polj. Izkazali sta se dve smiselni možnosti primerjave. Prva je primerjava poljubnih dveh slik pacienta za različna obdobja. Tako lahko zdravnik primerja slike želenih vidnih polj.4
Druga oblika primerjave pa temelji na primerjavi vidnega polja pacienta s povprečnim vidnim poljem (vidnim poljem zdravega človeka).
Slika 4 Primerjava vidnih polj (označenih z rdečo barvo).
Primerjava vidnih polj
Po izbiri ustreznega pacienta se uporabniku horizontalno izrišejo pomanjšane dvodimenzionalne slike vidnih polj v naraščajočem vrstnem redu glede na datum pregleda. Uporabnik si izbere poljubni dve sliki (slika 4), rezultat
76 Ikica A et al.: Primerjava 2-D in 3-D slik vidnega polja v načinu "threshold" z diferenčno metodo
primerjave pa se samodejno prikaže v posebnem oknu.
Slika 5 Ploskev sprememb v 2-D.
Pri primerjavi slik uporabljamo diferenčno metodo. Tako v vsakem slikovnem elementu (angl. pixel) izračunamo razliko vrednosti. Ta postopek nam vrne ploskev razlik. Kjer so razlike negativne, je prišlo do poslabšanja, drugje je vidno polje nespremenjeno. Za prikaz sprememb smo uporabili naslednji postopek: pozitivne razlike (ni poslabšanja) postavimo na ničlo, pri negativnih razlikah pa uporabimo absolutno vrednost. Tako dobimo invertirano ploskev, ki je pozitivno izbočena le v predelih, kjer je prišlo do poslabšanja. Večja kot je izbočenost, večje je poslabšanje vidnega polja. Sliki 5 ter 6 prikazujeta ploskev sprememb v 2-D in 3-D. Na levi strani je legenda, kjer svetlejša barva pomeni večjo spremembo.
Primerjava z normalo
Učinkovito orodje za odkrivanje poslabšanj vidnega polja je tudi primerjava vidnega polja z vidnim poljem statistične populacije ustrezne starosti. Zamislimo si pacienta, ki se mu skozi daljše časovno obdobje vidno polje ne slabša. V takšnem primeru bi prejšnja primerjava dala vtis, da je z vidnim poljem vse v redu. Izkaže pa se, da obstaja možnost, da je vidno polje že od samega začetka (prvi pregled) globoko pod povprečjem, kar je seveda patološki znak.
Slika 6 Ploskev sprememb v 3-D.
Vidno polje "povprečnega" pacienta se spreminja s starostjo, je določeno empirično in je prikazano v tabeli 1.
Tabela 1 Jakosti vidnega polja "povprečnega" človeka.
Starost	Jakost vidnega polja (dB)	
	V centru	Na periferiji
do 30 let	34 ali več	24 ali več
30 do 50 let	30 ali več	19 ali več
nad 50 let	26 ali več	15 ali več
Glede na starost pacienta program na podlagi tabele 1 interpolira ploskev "povprečnega" človeka (normalna ploskev). Vrednosti vidnega polja pacienta, ki leže pod njo, predstavljajo področja, kjer so poslabšanja.
Končna diagnoza je odvisna od zdravnika, saj rahlo poslabšanje ni nujno patološko. Pri odločanju o spremembah so pomembne izkušnje zdravnika. Pacienta ne moremo obravnavati kot matematični model, zato opisani postopki predstavljajo le zdravnikov pripomoček pri diagnosticiranju.
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Slika 7 Področja poslabšanja v 2-D.
Slika 8 Področja poslabšanja v 3-D (z rdečo je
označena norm:
ala).
Program HfaWin
Metode in mehanizmi, omenjeni v prejšnjih poglavjih, so implementirani v programu HfaWin, ki je nameščen v očesni ordinaciji Zdravstvenega doma Trbovlje.
Program je zasnovan tako, da omogoča neposredno povezavo s podatkovno bazo zdravstvenega doma preko šifre pacienta. To omogoča neodvisnost od HFA. Tako bi isti program lahko uporabili na napravah drugih proizvajalcev, le zajem je potrebno ustrezno prirediti. Ko so podatki iz naprave zajeti, jih obdela program HfaWin in jih poveže z ustrezno šifro pacienta. Tako lahko vedno in iz kateregakoli mesta osebje zdravstvenega doma pregleduje slikovno gradivo vidnega polja pacienta.
Slike 7, 8 in 9 prikazujejo zaslonske slike programa HfaWin.

Slika 9 Analiza kvadrantnih izpadov vidnega polja.
Zaključek
Prednosti programa HfaWin v primerjavi z originalnim programom, ki teče na napravi HFA, so številne. Prva je možnost povezave podatkov s podatkovno bazo ustanove. Druga prednost je fleksibilnost programa, saj ga lahko po želji uporabnika stalno modificiramo, dodajamo nove metode in mehanizme ter ga lahko optimiziramo po željah uporabnika.
Razvoj programa ni zaključen. Poleg slabosti, ki se bodo pokazale skozi čas (interakcija z uporabnikom), bomo dodajali nove metode avtomatičnega diagnosticiranja.
Na podlagi informacije o vidnem polju se da postaviti številne diagnoze. Zanimiv mehanizem, ki ga načrtujemo v prihodnosti, je diagnosticiranje na
78 Ikica A et al.: Primerjava 2-D in 3-D slik vidnega polja v načinu "threshold" z diferenčno metodo
podlagi informacije o kvadrantnih izpadih vidnega polja.
V primeru, da so jakosti vidnega polja v določenem kvadrantu blizu ničle (kvadrantni izpad), lahko na podlagi kombinacij kvadrantov, ki nastopajo, ugotovimo, kje je prišlo do okvare na vidni poti. Pri nekaterih kombinacijah kvadrantnih izpadov lahko diagnosticiramo različna možganska obolenja.
Literatura
1.	Pavišič Z: Oftalmologija. Medicinska knjiga Beograd-Zagreb 1971; 227, 231-235.
2.	Cupak K: Oftalmologija. Jugoslavenska medicinska naklada 1985; 205-215.
3.	Humphrey Field Analyser 650 — Owner's manual.
4.	Anderson DR: Automated Static Perimetry. Mosby Year Book 1992; 14-25, 32-39.
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Bilten SDMI ■
Na začetku mandata
Brez verodostojnih in ustreznih informacij je nemogoče pričakovati uspešen razvoj in učinkovito delovanje sistema zdravstvenega varstva. Informacije potrebujejo vsi, ki v ta sistem vstopajo oziroma so vanj vključeni in aktivno delujejo, ne glede na katerem nivoju. Seveda je vrsta in obseg informacij za posamezne partnerje različna. Načrtovalci zdravstvenega varstva in zdravstveni politiki potrebujejo drugačne informacije kot upravljavci ali izvajalci zdravstvene dejavnost ter tisti, ki so uporabniki tega sistema.
Tak, širok, pogled na zdravstveno informacijski sistem pa je potrebno še utrditi. Čeprav strokovnjaki že dolgo poudarjajo potrebo po celovitem gledanju na zdravstveno informacijski sistem, se predsodkov in etiketiranja v zvezi s tem kar ne moremo znebiti. Zdravstveno informacijski sistem še vedno prepogosto enačijo z računalništvom ali s programsko opremo, ki jo uporabljamo pri delu kot orodje. Vse kar je v zvezi s tem področjem naj bi pokrivali "računalničarji". Prepuščene so jim odgovornosti in naloge, ki jim niso kos. V zadnjem času "računalničarji", ki so neredko zelo dobro usposobljeni informatiki, vendar sami kljub temu ne morejo spremeniti odnosa do zdravstvene informatike, zavračajo tako stališče in številni med njimi niso več pripravljeni prevzemati odgovornosti, češ da je od njih odvisna zdravstvena informatika. Tisti, ki bi morali uporabljati zdravstveno informacijski sistem pri svojem delu in ga razvijati, da bi v večji meri služil učinkovitemu delovanju in razvoju področja, ki ga pokrivajo, se kljub vsemu še vedno radi izgovorijo, da se na računalništvo ne spoznajo. Tako opravičilo ni več moderno in prepričan sem, da bodo izgovori te vrste vedno bolj redki. Dosedanji razvoj pri nas in v svetu nas je prepričal, da je informatizacija družbe neobhodna in temu se tudi sistem zdravstvenega varstva, za katerega je znano, da je eden najbolj rigidnih, ne more več dolgo
upirati. Dosežki, ki jih beležimo na posameznih področjih pri nas, uspehi, o katerih poročajo v tujini in nenazadnje tudi informatizacija na drugih področjih družbenega življenja, s katerimi smo vsak dan v stiku, terjajo od nas odločne poteze.
Posebna prednost in moč našega društva je povezana z dejstvom, da se v njem srečujemo vsi, ki smo vključeni v procese sistema zdravstvenega varstva. Ne gre samo za sodelovanje in povezovanje različnih poklicev temveč tudi za sodelovanje različnih področij oziroma delov sistema zdravstvenega varstva v najširšem pomenu. Ne poznam društva, ki bi bilo v svojem poslanstvu tako kompleksno, ki bi združevalo toliko različnih poklicnih skupin in toliko različnih področij dela. Povezovanje članov in spodbujanje, da delujemo usklajeno pri razvoju medicinske informatike, pomeni izredno velik izziv, in vesel sem, da so mi članice in člani društva zaupali ter mi poverili mandat v sedanjem prelomnem obdobju. Storil bom vse, da bom to zaupanje upravičil in ga utrdil.
Raznolikost, ki jo predstavljajo članice in člani društva sprejemam kot izjemen potencial razvoja. Čim bolj širok spekter strokovnjakov in uporabnikov informacijskega sistema v zdravstvu bomo uspeli pritegniti in vključiti v delovanje našega društva, tem bolj bodo predlogi in rešitve, ki jih bomo v društvu pripravili in jih poskušali preko ustreznih organov, združenj in institucij udejanjiti, zanesljivi, kredibilni in tudi sprejemljivi. Posebej bomo skrbeli za sodelovanje z uporabniki zdravstveno informacijskega sistema. Glede na to, da so informacije potrebne vsem, ki so vključeni v sistem zdravstvene dejavnosti, bomo ustrezno informatizacijo lažje zagotavljali, če bodo uporabniki informacij pri načrtovanju in pridobivanja podatkov sodelovali. Neprecenljive so izkušnje posameznikov, ki delajo na določenih
80
Eržen I: Na začetku mandata
področjih in iz lastnega zornega kota opozarjajo na pomanjkljivosti in posredujejo koristne predloge.
SDMI mora omogočiti delovanje širokega strokovnega foruma, ki bo v končni fazi dovolj konstruktiven in bo posredoval najboljše rešitve -predvsem v smislu zadovoljevanja dolgoročnih potreb sistema zdravstvenega varstva po informacijah, ob tem pa upošteval možnosti, ki nam jih ponuja sodobna informacijska in komunikacijska tehnologija. Uporaba sodobnih rešitev na področju informacijske in komunikacijske tehnologije in storitev v zvezi s tem predstavlja, skupaj z organizacijskimi spremembami in razvojem novih veščin, temeljno orodje za povečanje učinkovitosti zdravstvenega sistema. Ni mogoče razmišljati o izboljšanju dostopnosti in kakovosti zdravstvene dejavnosti ter učinkovitosti in produktivnosti zdravstvenega sistema, če ne bomo dosegli bistvenih premikov na področju odnosa do razvoja in uporabe informatike v zdravstvu.
SDMI bo imel pri tem izredno pomembno vlogo. V letih svojega obstoja je bilo društvo vedno pomemben partner pri dogovarjanju in razvoju tega specifičnega področja. V sedanjem času pa je naša naloga še posebej pomembna in zahtevna. Društvo mora preko svojih članic in članov postati tvorni sooblikovalec ciljev, strategije in načrta aktivnosti za implementacijo e-zdravja v našem prostoru. To zahteva od nas v naslednjih letih posebej zavzeto delo, vendar sem prepričan, da bomo nalogam kos, zlasti če bomo zagotovili tesno sodelovanju vseh delov zdravstvenega sistema
(nosilci odločitev na področju zdravstva, zdravstveni delavci in sodelavci, potrošniki oziroma bolniki, industrija).
Članice in člane društva bom spodbujal, da bodo sprejeli društvo kot prostor, kjer bo priložnost za kresanje mnenj pa tudi kot strokovno kompetentno in dovolj močno skupino, ki je sposobna prepričanje udejanjiti v širši družbeni skupnosti. Nič na ne pomaga poznavanje razmer in vedenje, s katerim rešitvami bi zagotovili hitrejši in ustrezen razvoj informacijskega sistema, če tega poznavanja ne bomo uspeli tudi udejanjiti. Trudili se bomo, da bomo k sodelovanju pritegnili čim več novih članic in članov ter simpatizerjev, ki nam bodo pomagali. Pri tem bomo bolj uspešni, če bomo uspeli poslanstvo in delovanje našega društva strokovnjakom, ki delujejo v okviru sistema zdravstvenega varstva kot tudi širše v družbi, čim bolje predstaviti. Zaradi tega smo se odločili, da bomo promociji delovanja društva namenili posebno pozornost in že zadolžili člana upravnega odbora, ki bo koordiniral to nalogo.
Članice in člani SDMI so aktivni, motivirani in pripravljeni na sodelovanje. Skrbel bom za to, da bomo delovali čim bolj usklajeno in bomo povezovali tiste, ki sodelujejo pri ustvarjanju informacij, ter tiste, ki jih potrebujejo. Z veliko mero optimizma gledam na te zadolžitve in sem prepričan, da bomo uspešni.
Dr. Ivan Eržen, predsednik SDMI
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Zaključki
strokovnega srečanja E-zdravje za boljše zdravje v Sloveniji, Zreče, 17.-18.6.2005
Uvod
17. in 18. junija 2005 je Slovensko društvo za medicinsko informatiko v Zrečah organiziralo strokovno srečanje z naslovom E-zdravje za boljše zdravje v Sloveniji. Preko 70 udeležencev iz vrst politike, managementa v zdravstvu, akademske sfere, zdravstvenih delavcev in informatikov je zavzeto razpravljalo o nujnih korakih za nadaljnji razvoj zdravstvene informatike oz. rešitev e-zdravja.
Dokument povzema razpravo in zaključke delavnic tega srečanja, na katerih je bilo ugotovljeno sedanje stanje in jasno začrtane smernice za nadaljnji razvoj tega področja. Društvo pošilja dokument Ministrstvu za zdravje z namenom, da pomaga pri oblikovanju nacionalnega akcijskega načrta in da spodbudi bistveno hitrejši razvoj na tem, za zdravstvo izredno pomembnem področju.
Povzetek stanja
Slovenija je na področju uporabe informacijske in telekomunikacijske tehnologije v preteklosti že beležila nekatere pomembne dosežke. Vzpostavljene so pomembne baze podatkov, subjekti v zdravstvu razpolagajo z lastnimi informacijskimi sistemi, uvedene so rešitve elektronskega izmenjevanja podatkov,
vzpostavljene so nekatere internetne rešitve za občane in zdravstvene delavce.
Uspešno izvedeni projekti so jasno pokazali več kot pozitiven vpliv na uspešnost delovanja zdravstvenega sistema. Večji projekti so močno odmevali v Evropski uniji.
A v zadnjih letih je jasno razvidno, da beležimo upočasnitev razvoja, na nekaterih področjih kar zastoj pri razvoju zdravstvenega informacijskega sistema. Posebej kritično je stanje na področju informacijske podpore strokovno medicinskemu delu v primarnem zdravstvu.
Obseg sredstev, namenjenih za informatiko je pri izvajalcih zdravstvenih storitev tako nizek (v povprečju dosega po oceni le 1% sredstev za zdravstveno varstvo), da ogroža zadovoljivo vzdrževanje sistema in onemogoča nadaljnje razvojne korake. Informacijska oprema je zastarela. Razvoj infrastrukture sistema in skupnih razvojnih projektov na nacionalni ravni ni koordiniran. Nezadostna koordinacija je vedno večja ovira za uspeh pri sodobnih rešitvah e-zdravja, ki terjajo učinkovito podatkovno integracijo med posameznimi dejavnostmi in nivoji zdravstva. Čeprav so nekateri razvojni projekti dobro definirani, ne najdemo konsenza za njihovo podporo in izvedbo. Pri koriščenju sredstev iz evropskih skladov in programov nismo uspešni.
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Usmeritve Evropske unije
Evropska unija je v akcijskem načrtu za e-zdravje, sprejetem v lanskem letu, jasno opredelila pomen rešitev e-zdravja, kot enega ključnih dejavnikov za uspešen nadaljnji razvoj zdravstvenega sistema v državah članicah unije.
Vse države članice so se zavezale slediti skupnim usmeritvam in zagotavljati predpogoje za skupne naloge kot so zagotavljanje mobilnosti bolnikov in zdravstvenih delavcev, izboljševanje nadzora in obrambe pred nevarnostmi za zdravje, izboljševanje dostopnosti informacij, pomembnih za krepitev zdravja.
Akcijski načrt obvezuje države članice, da v letu 2005 pripravijo in zagotovijo začetek uresničevanja svojih nacionalnih strategij. Tudi Slovenija je pred to zahtevno nalogo.
Strategija in koordinacija razvoja zdravstvenega informacijskega sistema
Razvojna strategija e-zdravja
Nacionalna strategija za e-zdravje mora temeljiti na strategiji razvoja celotnega sistema zdravstvenega varstva (nacionalni program zdravstvenega varstva) z jasno opredeljenimi vsebinami in prioritetami. Zaradi novih družbenih okoliščin, usmeritev Evropske unije in novih priložnosti bi bilo potrebno revidirati obstoječo strategijo zdravstva.
Razvoj informacijskih rešitev za podporo strokovnemu delu zdravnika (še posebej na primarnem nivoju) in farmacevta mora imeti v strategiji e-zdravja najvišjo prioriteto. V tem trenutku delo poteka pretežno s papirjem in svinčnikom, kar postaja neobvladljivo in nevarno.
Nacionalni organ
Kot izredno nujna in ključna naloga se kaže ustanovitev nacionalnega organa za razvoj zdravstvene informatike, ki mora delovati pod okriljem Ministrstva za zdravje in ki mora:
•	skrbeti za oblikovanje strategije razvoja zdravstvene informatike,
•	koordinirati razvoj novih projektov in nadaljevanje že začetih projektov,
•	načrtovati in koordinirati uvajanje skupne infrastrukture,
•	koordinirati nastajanje in sprejem standardov,
•	določati kriterije in standarde za varovanje in zagotavljanje kakovosti informacij v zdravstvenih informacijskih sistemih.
Brez takšnega nacionalnega načrtovanja in usklajevanja bo v bodoče vedno težje izkoriščati prednosti, ki jih prinaša informacijska in telekomunikacijska tehnologija in ki so ključnega pomena za izboljševanje produktivnosti, kakovosti in dostopnosti zdravstva. Brez takšnega pristopa ne bo mogoče zadostiti pogojem in slediti usmeritvam Evropske unije.
Standardizacija
Ena ključnih nalog organa za usklajevanje razvoja zdravstvene informatike je pravočasno zagotavljanje potrebnih standardov — tako razvoj nacionalnih, kot privzemanje mednarodnih standardov.
Najpomembnejša področja, ki zahtevajo standardizacijo so:
•	zagotavljanje enotnih modelov podatkov oz. t.im. podatkovnih slovarjev pri čemer je potrebno najprej zagotoviti standarde za skupna, temeljna področja,
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•	zagotavljanje enotnosti izmenjevanja podatkov (načini posredovanja podatkov, formati podatkov, načini šifriranja, idr.),
•	varnostni standardi — oblikovati je potrebno generične modele varnosti za posamezne vrste izvajalcev sledeč mednarodnim standardom.
Varnost informacij
Poleg priprave varnostnih standardov je potrebno zagotoviti njihovo uresničitev pri subjektih v zdravstvu.
Standardne rešitve za varovanje informacij je potrebno vgrajevati v vse razvojne projekte.
Skrbeti je potrebno za stalno izobraževanje in dvigovanje zavesti glede varovanja informacij pri zdravstvenih delavcih in managementu.
Financiranje
Nujno je potrebno zagotoviti bistveno več finančnih sredstev za informatiko na vseh ravneh sistema zdravstvenega varstva.
Za razvoj infrastrukture in izvajanje projektov državnega pomena, je potrebno zagotoviti pomemben del sredstev iz državnega proračuna. Dodatno je potrebno iz sredstev za zdravstvene programe in storitve izdvojiti namenska sredstva za razvoj in kakovostno vzdrževanje informacijske opreme in rešitev.
Za vsak razvojni projekt je potrebno pripraviti celovito analizo stroškov in koristi in zagotoviti sredstva ne le za razvoj rešitev in nakup opreme, ampak tudi za njihovo redno vzdrževanje.
Ministrstvo mora zagotoviti urejeno financiranje nacionalnega koordinacijskega organa z vsemi potrebnimi enotami.
Organi, ki so pristojni za usklajevanje in spodbujanje koriščenja finančnih sredstev iz skladov in programov evropske unije, morajo omogočati in pomagati subjektom v zdravstvu, da bodo lahko kandidirali za ta sredstva za potrebe razvoja rešitev e-zdravja.
Nujna razvojna področja
Pristop
Razvojne korake je potrebno izpeljati v obliki razvojnih projektov, pri čemer je potrebno upoštevati metodologijo projektnega vodenja. Pri kompleksnih projektih je potrebno predhodno pripraviti in dogovoriti zasnovo novega sistema ter jo preveriti z izvedbo pilotskih projektov.
Ključno vlogo pri načrtovanju in razvoju rešitev za zdravstvene delavce naj imajo zdravstveni delavci sami. Zagotoviti in motivirati je potrebno njihovo pravočasno in aktivno vključevanje v projekte. K sodelovanju je potrebno vključiti "pionirje" na področjih uvajanja uporabnih rešitev e-zdravja.
Zdravstvenim delavcem in managementu v zdravstvu je potrebno zagotoviti pravočasno izobraževanje o novostih, ki jih prinašajo novi projekti.
Elektronski zdravstveni zapis in uporaba računalnika pri strokovnem delu zdravnika
Najvišjo prioritetno morajo imeti projekti vzpostavljanja elektronskih zdravstvenih zapisov, ki morajo nadomestiti obstoječ papirni zdravstveni karton in drugo papirno medicinsko dokumentacijo.
Elektronski zdravstveni zapis je potrebno graditi fazno. Razvoj vsake faze je potrebno podrobno načrtovati, vsebino verificirati v okviru ustrezne stroke in standardizirati.
V končni obliki mora elektronski zdravstveni zapis poleg medicinskih podatkov vsebovati tudi
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podatke drugih strok, ki skrbijo za celostno zdravje bolnika (zdravstvena nega, sociala, ...).
Rešitve morajo omogočati bolnikom dostop do lastnih podatkov v elektronskem zdravstvenem zapisu in omogočati seznanjanje bolnika o poteku zdravljenja (elektronsko obveščanje o izvidih, elektronsko naročanje).
eRecept
Čim preje se je potrebno opredeliti do projekta eRecept kot prioritete v razvoju in doseči nacionalni konsenz o zasnovi sistema in pričetku projekta. Po mnenju zdravnikov, ki so sodelovali v razpravi eRecept predstavlja 1/3 elektronskega zdravstvenega kartona in prispeva k večji varnosti pacientov v postopku zdravljenja ter k zmanjšanju stresne obremenjenosti zdravnikov.
Ključne vsebine projekta so:
•	zagotoviti pravne podlage,
•	razviti postopke pri delu zdravnika in farmacevta,
•	revidirati njihovo vlogo pri predpisovanju in izdaji zdravil;
•	zagotoviti ekspertni sistem z vsemi potrebnimi podatki o zdravilih — vzpostavitev slovenske baze ali prevzem tuje rešitve, ki mora biti povezana z registracijskim postopkom, zagotoviti je potrebno organizacijsko umestitev in zagotoviti njeno redno vzdrževanje.
Paralelno je potrebno zagotoviti osnovno informacijsko infrastrukturo — računalnik in čitalnik pri zdravniku, vzpostavitev in umestitev strežnika za izdane recepte, vzpostavitev povezav iz okolja izvajalcev, zagotovitev varnosti pri komuniciranju — digitalnega podpisa.
Enolična identifikacija zdravstvenih delavcev
Zagotoviti je potrebno varno infrastrukturo za elektronsko poslovanje zdravstvenih delavcev. Za uveljavitev elektronskega zdravstvenega zapisa in eRecepta je potrebno zagotoviti mehanizem za enolično identifikacijo zdravstvenih delavcev, za preverjanje avtentičnosti elektronski dokumentov, za šifriranje in elektronsko podpisovanje elektronskih dokumentov.
Za te potrebe je potrebno zdravstvenim delavcem zagotoviti uporabo kvalificiranih digitalnih potrdil. Potrebno je vztrajati, da se uporabljajo digitalna potrdila, ki so hranjena na pametnih karticah, ker ta tehnologija zagotavlja potrebno obrambo pred aktualnimi varnostnimi tveganji. Do takšne rešitve je smiselno priti z nadgradnjo obstoječe profesionalne kartice sistema KZZ.
Izdajo digitalnih potrdil je potrebno vezati na bazo podatkov o izvajalcih in druge nacionalne evidence zdravstvenih delavcev (Zdravniška zbornica, Lekarniška zbornica, ...), saj je smiselno, da digitalno potrdilo poleg identifikacije zdravstvenega delavca zagotavlja tudi informacijo o njegovem statusu, specializaciji in druge informacije pomembne za omejevanje dostopov do posameznih virov podatkov.
Zagotavljanje temeljnih baz podatkov
Zagotoviti je potrebno rešitve za distribucijo temeljnih baz podatkov zdravstvenega sistema k izvajalcem zdravstvenih storitev. Trenutno se kot najbolj aktualna kratkoročna naloga kaže zagotavljanje podatkov iz baze podatkov o izvajalcih (BPI), ki jo vodi Inštitut za varovanje zdravja.
Zdravstveno omrežje
Nove rešitve e-zdravja temeljijo na vse večjem obsegu izmenjevanja podatkov med informacijskimi subjekti v zdravstvu. Zagotoviti je potrebno zanesljive, zmogljive in varne telekomunikacijske zmogljivosti. Dinamiko razvoja
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te infrastrukture je potrebno prilagoditi zahtevam projektov oz. rešitev, ki jih bodo pripravili ti projekti.
Izrednega pomena je torej pravočasno in podrobno načrtovanje telekomunikacijskih potreb za rešitve, ki bodo potrebovale takšno infrastrukturo.
Zdravstveni portali
Slovenija potrebuje ob specifičnih portalih, namenjenih posredovanju informacij z zdravstveno vsebino tudi nacionalno vstopno točko do vseh zaupanja vrednih in kvalitetnih tovrstnih informacij.
Podpirati je potrebno obstoj delujočih dobrih portalov (denarna sredstva, človeški viri).
Potrebno je določiti temeljne kriterije za vrednotenje zanesljivosti in veljavnosti informacij z zdravstvenimi vsebinami.
Vzpostaviti je potrebno eno ali več neodvisnih strokovnih skupin za vrednotenje in preverjanje kakovosti vsebin portalov in spletnih strani z zdravstvenimi vsebinami na osnovi sprejetih kriterijev.
Nacionalna vstopna točka mora skrbeti, da bodo javnosti na voljo vse vsebine, ki so pomembne za njihovo informiranost s področja skrbi za zdravje.
Potrebno je zagotoviti, da bodo institucije za informacije, ki so jih pridobile ali izdelale s pomočjo javnih sredstev, omogočile ažuren dostop preko omenjene vstopne točke.
Trenutni predpisi v Sloveniji marsikdaj onemogočajo dostop do informacij z zdravstvenimi vsebinami. Potrebno jih je pretehtati in po potrebi spremeniti.
Sodobne elektronske rešitve
Poleg zgoraj navedenih projektov, je potrebno določeno pozornost posvečati tudi sodobnim
rešitvam, ki omogočajo dostopnost zdravstvenih storitev na daljavo kot so telemedicina na domu in oskrba bolnika na domu na daljavo.
Zagotoviti je potrebno ustrezne organizacijske oblike za nudenje tovrstnih storitev in poleg opreme in rešitev zagotoviti usposabljanje in motiviranje zdravstvenih delavcev.
Podpora projektom v teku
Nacionalni organ za koordinacijo razvoja rešitev e-zdravje mora pripraviti evidenco obstoječih projektov, oceniti pomen teh projektov ter projektom, za katere oceni, da so pomembni za razvoj zdravstva, pomagati pri nadaljnjih nalogah.
Zakonske podlage
Projekti v teku čutijo številne ovire v obstoječi zakonodaji. Nujno je potrebno pravočasno prenavljati Zakon o evidencah s področja zdravstva in na novo opredeliti zbirke podatkov, njihovo vsebino, namen in upravljavce. Posebno pozornost je potrebno nameniti opredelitvam omejitev dostopa za posamezne skupine uporabnikov (strokovno medicinska uporaba, finančno-upravna uporaba, dostopnost za najširšo javnost).
Zagotoviti je potrebno zadostne vire, da se za projekte v teku pravočasno zagotovijo potrebne zakonske podlage oz. spremenijo obstoječe. Ključno vlogo pri tem mora imeti Ministrstvo za zdravje.
Člani programskega odbora srečanja: Tomaž Marčun, Gregor Cerkvenik, Leo Ciglenečki, Ivan Eržen, Brane Leskošek, Jožica Leskovšek, Dorjan Marušič, Drago Rudel, Smiljana Slavec, Jože Zrimšek
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BÓ
Oglasi