Quantitative Analysis of Signal Abnormalities in MR Imaging

89 Pages·2001·1.51 MB·English

KATHOLIEKE UNIVERSITEIT LEUVEN
FACULTEIT TOEGEPASTE WETENSCHAPPEN
DEPARTEMENTELEKTROTECHNIEK (ESAT),
AFDELINGPSI
KasteelparkArenberg10,3001Leuven-Heverlee (Belgium)
FACULTEIT GENEESKUNDE
DEPARTEMENTMORFOLOGIEEN MEDISCHE
BEELDVORMING,AFDELINGRADIOLOGIE
Herestraat49,3000Leuven(Belgium)
Quantitative Analysis of Signal
Abnormalities in MR Imaging
for Multiple Sclerosis and Creutzfeldt-Jakob Disease
Promotoren: Proefschrift voorgedragen tot
prof. Dr.ir. D.Vandermeulen het behalen van het doctoraat
prof. Dr.ir. P.Suetens indetoegepastewetenschappen
door
Koen VAN LEEMPUT
18 mei 2001

KATHOLIEKE UNIVERSITEIT LEUVEN
FACULTEIT TOEGEPASTE WETENSCHAPPEN
DEPARTEMENTELEKTROTECHNIEK (ESAT),
AFDELINGPSI
KasteelparkArenberg10,3001Leuven-Heverlee (Belgium)
FACULTEIT GENEESKUNDE
DEPARTEMENTMORFOLOGIEEN MEDISCHE
BEELDVORMING,AFDELINGRADIOLOGIE
Herestraat49,3000Leuven(Belgium)
Quantitative Analysis of Signal
Abnormalities in MR Imaging
for Multiple Sclerosis and Creutzfeldt-Jakob Disease
Leden van de jury: Proefschrift voorgedragen tot
prof. Dr.ir. J. Berlamont (voorzitter) het behalen van het doctoraat
prof. Dr.ir. D.Vandermeulen (promotor) indetoegepastewetenschappen
prof. Dr.ir. P.Suetens(promotor) door
prof. Dr.ir. F. Maes
prof. Dr.ir. S.Van Huffel Koen VAN LEEMPUT
prof. Dr.ir. J. Nuyts
prof. Dr.P. Demaerel
prof. Dr.W. Van Paesschen
prof. Dr.ir. G. Gerig (UNCChapel Hill)
U.D.C. 615.849
18 mei 2001
(cid:1)c Katholieke Universiteit Leuven− Faculteit Toegepaste Wetenschappen
Arenbergkasteel, Kasteelpark Arenberg 1, B-3001 Heverlee (Belgium)
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ISBN 90-5682-299-3
D/2001/7515/13
Theory is important
– at least in theory
(K. Martin)

Acknowledgements
Thisthesisistheresultofmanyinvisiblehandshelpingme. Specialthanksgotomy
supervisorandmentorDirkVandermeulen. His uniquewayofsteeringmyresearch
by seemingly accidentally leaving papers on my desk and by giving subtle hints at
the rightmoment, keptme onsteady course. I amconvincedthat he will recognize
manyofhisoriginalideasinoneformoranotherwhilebrowsingthroughthisthesis.
My gratitude also to my other supervisor, Paul Suetens, for having given me the
opportunity to work in his internationally recognized group and for taking care of
everything that could have diverted me from my research.
Iamdeeplyappreciativetothesecondauthorofmypublications,FrederikMaes.
Hiswillingnesstopatientlyreadandre-readmydraftsandhisabilitytomercilessly
filteroutweakpointshavegreatlyimprovedthequalityofmypapers. Workingand
discussing with him has been a great pleasure and a very instructive experience.
During the often memorable BIOMORPH and QAMRIC meetings, I have had
the pleasure to get to know some of the leading researchers in the field. Amongst
them, I would like to thank Alan Colchester, Nicholas Ayache, Mike Brady and
Guido Gerig for their interest and for their many useful comments. Special thanks
also to Eric Bardinet and Nicholas Ayache for my most enjoyable stay with the
Epidaure group at INRIA Sophia-Antipolis last September.
I am grateful to my colleagues and to all the people somehow involved in the
Leuven group for the refreshing and open atmosphere (figuratively speaking, that
is). Special thanks go to all those who have ever helped me with one of my many
computer problems. Since computer-related topics are not my strongest side, I am
afraid that includes almost everyone.
IthanktheInstituteforthePromotionofInnovationbyScienceandTechnology
in Flanders (IWT) for its financial support.
A special wordof thank goes to the people from what is now calledthe medical
imagingresearchgroupinOulu,Finland. Inparticular,I wouldliketo thankLasse
Jyrkinen and Olli Silv´en for helping me on my first steps in the field of medical
imaging. NotonlydotheyliedirectlyonthebasisofthisPhDbyinfectingmewith
the researchvirus at the time of my MSc thesis, their idea of having me come over
during the summer of 1996 also changed my life in many other ways as well.
Addictive and fascinating as the researchpartof this PhD was,the writing was
tediousanddifficult. Iwouldthereforeliketothankmyfamilyfortheirsupport: my
caringparents;mybrotherDirk,whohasbeenjokinglyremindingmetomentionhis
nameinthisacknowledgementeversinceheheardthatIhadstartedwritingup;his
girlfriendYvonne;andespeciallymygirlfriendElena. Elena,yourenthusiasm,your
many brilliant ideas, your patient listening to my frustrations, your encouraging
words and your strong belief in the formability of life, make you the ghostwriter of
this thesis. Thank you for always being there for me. Thank you for painting my
life in the most beautiful colors.
Leuven, May 4, 2001
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Abstract
Quantitative analysis of signal abnormalities in MR imaging
for multiple sclerosis and Creutzfeldt-Jakob disease
InmultiplesclerosisandCreutzfeldt-Jakobdisease,pathologicalchangesinthebrain
resultindeviationsinsignalintensityonMRimages. Quantitativeanalysisofthese
changes and their correlationwith clinical findings provides important information
for in vivo diagnosis, therapy planning and follow-up as well as for biomedical
research. This thesis presents a framework for fully automated, objective and re-
produciblequantificationofpathology-relatedsignalabnormalitiesinlargeamounts
of multi-spectral MR data. The overall strategy adopted was to build statistical
models for normal brain MR images, including explicit models for MR field inho-
mogeneities,tissue-specificintensitydistributions,spatialclusteringoftissuetypes,
andthepartialvolumeeffect. Signalabnormalitiesweredetectedasmodeloutliers,
i.e. voxels that could not be well explained by the model. Throughout this thesis,
specialattentionwaspaidto automaticallyestimate allmodelparametersfromthe
data itself, to eliminate subjective manual tuning and training.
Kwantitatieveanalyse van signaalabnormaliteiten inMR-beeld-
vorming voor multiple sclerose en Creutzfeldt-Jakob
Bij multiple sclerose en de ziekte van Creutzfeldt-Jakob resulteren pathologische
veranderingenindeherseneninafwijkingenindesignaalintensiteitopMR-beelden.
Kwantitatieveanalysevandezeveranderingenenhuncorrelatiemetklinischebevin-
dingen levert belangrijke informatie op voor zowel in vivo diagnose, therapieplan-
ning en opvolging als voor biomedisch onderzoek. Deze thesis stelt een raamwerk
voor voor volledig automatische, objectieve en reproduceerbare kwantificatie van
pathologische signaalafwijkingen in grote hoeveelheden multi-spectrale MR-data.
De algemene strategie die werd gevolgd, bestond uit het construeren van statisti-
sche modellen voor MR-beelden van normale hersenen, met inbegrip van expliciete
modellen voor MR-veldinhomogeniteiten, weefselspecifieke intensiteitsverdelingen,
spatialeclusteringvanweefseltypes,enhetpartieel-volume-effect. Signaalabnorma-
liteitenwerdengedetecteerdalsvoxelsdienietgoeddoorhetmodelkondenworden
verklaard. Doorheen deze thesis werd er bijzondere aandacht geschonken aan het
automatisch schatten van alle modelparameters uit de data zelf, om subjectieve
manuele interventie uit te sluiten.
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