Longitudinal analysis of MRI images in rheumatoid arthritis

183 Pages·2007·4.31 MB·English

Longitudinal analysis of MRI images in
rheumatoid arthritis
Kelvin Ka-fai Leung
Adissertationsubmittedinpartialfulfillment
oftherequirementsforthedegreeof
DoctorofPhilosophy
ofthe
UniversityofLondon.
CentreforMedicalImageComputing
DepartmentofComputerScienceandDepartmentofMedicalPhysics
UniversityCollegeLondon
November16,2007
2
I,KelvinKa-faiLeung,confirmthattheworkpresentedinthisthesisismyown.Whereinformation
hasbeenderivedfromothersources,Iconfirmthatthishasbeenindicatedinthethesis.
3
Tomyparents
Abstract
Rheumatoidarthritis(RA)isachronic,systemic,autoimmune,andinflammatoryjointdisease. Longi-
tudinalimagingusingMRIhasbeenproposedasaneffectivetoolforpatientmanagementandclinical
studies, but the analysis is based on visual inspection or interactive analysis. This thesis investigates
image registration and analysis algorithms to automatically quantify changes in a bone in in-vivo lon-
gitudinal MR images of an ankle in an experimental rat model of RA, and to improve the ease of use,
reproducibility,andanalysistimeofvisualscoringsystemsinthehandsandwristsofhumanpatients.
Inthefirstpartofthisthesis,twodifferentmethodswereusedtoautomaticallyquantifychangesin
aboneintheexperimentalmodelofRA.Thefirstmethodusedsegmentationpropagationtodelineatea
bonefromthelongitudinalMRimagesgivingaglobalmeasureoftemporalchangesinbonevolume.The
secondmethodusedrigidbodyregistrationtodetermineintensitychangewithinaboneusinganintensity
thresholding algorithm, and then mapped these into a common space using nonrigid registration. This
gave a local measure of temporal changes in bone lesion volume. The global bone volume was found
to be fluctuating over time. However, significant temporal changes were detected in local bone lesion
volume in 4 out of 8 identified candidate bone lesion regions, and significant difference in local bone
lesionvolumebetweenmaleandfemalesubjectswasdetectedin3outof8candidatebonelesionregions.
Someoftheresultswereconfirmedbycomparisonwiththehistologyofthesubjects.
The second part of this thesis described an integrated spatio-temporal segmentation algorithm,
which built on the results of the first part that bone lesions are localised in space, time, and intensity
of the difference images. A 5-dimensional feature space (3 spatial dimensions, 1 intensity dimension,
and 1 temporal dimension) was built from the longitudinal MR images after rigid body registration to
integratethetime-domaininformationacrossallthetimepoints. Thefeaturespacewasthendelineated
by the mean shift algorithm to give high-intensity bone lesions as 4D segmentations. This technique
was quantitatively compared with the previous method using simulated and real MR images, and was
qualitativelycomparedwiththehistologyofthesubjects.
Inthethirdpartofthisthesis,imageregistrationwasusedtoaligneachboneintheMRimagesof
thehandsandwristsofhumanpatientstoacommonreferencecoordinatesystem,basedonareference
atlassuchastheEULAR-OMERACTrheumatoidarthritisMRIreferenceimageatlas.Thismethodmay
beusedtoincreasethesensitivityofvisualscoringtosubtlechangesbyreducingtheimpactofvariation
inposition,andcouldprovideapre-processingstepforautomaticquantificationoferosionorsynovitis.
MedicalresearchanddrugdiscoveryrelyincreasinglyoncomparisonsbetweenMRimagesfrom
Abstract 5
large numbers of subjects, often with multiple time points for each subject. In the final part of this
thesis, the computational grid (the Grid), which provides an infrastructure for researchers in different
organisationstosharetheirworkandfacilities(e.g. imagedataandcomputingresources),wasusedto
executethealgorithmsremotelyandinparallel. Thealgorithmsinthefirstthreepartsofthisthesiswere
expressedinaworkflowlanguageprovidedbytheVirtualDataSystemsothattheirexecutionwasfully
automated. The system was also designed to keep track of data provenance that provides information
abouttheoriginofapieceofdataandtheprocessbywhichitwasderived. Thisinformationiscrucial
fortheunderstandingandvalidationofscientificresults,andcanbeusedtoautomaticallygeneratethe
audittrailrequiredinthehandlingofelectronicrecordsinapharmaceuticalcompany.
Acknowledgements
IwouldliketoexpressmysincerethankstomyfirstsupervisorProf. DerekHill. forhisguidanceand
encouragement throughout my three years at KCL and UCL. My thanks are due to Dr. Mark Holden
forhisguidanceanddiscussionsduringtheearlystagesofthisproject. Iamgratefulformyindustrial
supervisorDr. NadeemSaeedfordiscussionsandhiscareduringmyvisitstoGlaxoSmithKline(GSK).
IalsothankothercolleaguesinGSKforthepreparationoftheexperimentalmodelandtheimageacqui-
sition. ThankstoDr. KateMcLeishfortheimageacquisitionofRApatients,andDr. StephenOakley
andDr. BruceKirkamforthepatientrecruitment,clinicalassessmentandvariousworkintheRAstudy.
IamgratefultomysecondsupervisorProf. DavidHawkesforhiscommentsonmythesis. Thanksalso
toProf. JoHajnalandProf. DanielRuckertformanyinterestingdiscussionsandcommentsonmywork
duringtheIXImeetings. ThankstomyEnglishtutorDr. SimonWilliamsforhelpingmetowritebetter
English. ImustalsothankothermembersofthedepartmentinKCLandUCLfordiscussionsandhelp.
Manythankstomyfamilyandfriendsfortheirsupportduringthisproject. Inparticular,Iwouldliketo
thankmywifeAnnieandmysonBryanfortheirpatienceandsupportduringthisproject.
Contents
1 Introduction 17
1.1 Backgroundandmotivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.2 Imageregistration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.3 Thesisaims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.3.2 Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.4 Thesisorganisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.5 Softwareandothermaterials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.6 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.6.1 Overall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.6.2 Individualchapters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2 Literaturereview 24
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2 ImagingofjointsinRA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2.1 Conventionalradiography(CR) . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2.2 Microfocalx-ray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.3 ComputedTomography(CT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.4 Bonescintigraphy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.5 Magneticresonanceimaging(MRI) . . . . . . . . . . . . . . . . . . . . . . . . 26
2.2.6 Ultrasound(US) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3 Measuringchangesinlongitudinalimages . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.3.1 Visualexaminationandscoring . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.3.2 Changeinvolume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.3.3 Subtractionafterregistrationandtheboundaryshiftintegral . . . . . . . . . . . 28
2.4 Imageregistration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.4.1 Thefeature-basedimageregistrationalgorithm . . . . . . . . . . . . . . . . . . 29
2.4.2 Theintensity-basedimageregistrationalgorithm . . . . . . . . . . . . . . . . . 30
2.5 Supervisedandunsupervisedclassifications . . . . . . . . . . . . . . . . . . . . . . . . 37
2.5.1 Unsupervisedclassification(ordataclustering) . . . . . . . . . . . . . . . . . . 37
Contents 8
2.6 AnimalModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.7 Histologicalanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3 AutomaticQuantificationofChangesinLongitudinalMRImagesofJointsI 46
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.2 Aimsandcontribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.4 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.4.1 Segmentationpropagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.4.2 Otsu’sthresholding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.4.3 Mathematicalmorphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.5 Overviewsofmethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.5.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.5.2 Assumptionsandobservations . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.5.3 Overviewofthemeasurementofchangesinbonevolume(Method1) . . . . . . 53
3.5.4 Overviewofthemeasurementofchangesinbonelesionvolume(Method2). . . 53
3.6 Materialsandmethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.6.1 Imagedata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.6.2 Atlasconstruction(usedinMethods1and2) . . . . . . . . . . . . . . . . . . . 54
3.6.3 Approximatedelineationofthetalusbone(usedinMethods1and2) . . . . . . 54
3.6.4 Accuratedelineationofthetalusbone(usedinMethods1and2) . . . . . . . . . 56
3.6.5 Generationofdifferenceimages(usedinMethod2) . . . . . . . . . . . . . . . 57
3.6.6 Identificationofpotentiallesionvoxels(usedinMethod2) . . . . . . . . . . . . 57
3.6.7 Identificationofcandidatebonelesionregions(usedinMethod2) . . . . . . . . 57
3.6.8 Calculationofbonelesionvolume(usedinMethod2) . . . . . . . . . . . . . . 58
3.7 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.7.1 Method1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.7.2 Method2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.8 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.8.1 Method1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.8.2 Method2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.9 Discussionandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4 AutomaticQuantificationofChangesinLongitudinalMRImagesofJointsII 77
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.2 Aimsandcontribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.3 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.4 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.4.1 Meanshift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Contents 9
4.5 Materialsandmethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.5.2 Imagedata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.5.3 Atlasconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.5.4 DeterminationofROI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.5.5 Generationofdifferenceimages . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.5.6 Spatio-temporalsegmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.5.7 Identificationofcandidatebonelesionregions . . . . . . . . . . . . . . . . . . 82
4.5.8 Calculationofbonelesionvolume . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.5.9 Speedimprovementofthemeanshiftsegmentationbysubsampling . . . . . . . 83
4.5.10 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.5.11 Comparisonwithmanualsegmentationofthebonelesions . . . . . . . . . . . . 84
4.6 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.6.1 Experimentsonsimulateddata . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.6.2 Experimentsonrealdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.6.3 Comparisonwithhistology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.6.4 Comparisonwithmanualbonelesionvolume . . . . . . . . . . . . . . . . . . . 93
4.6.5 Runningtime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.7 Discussionandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5 ApplicationtoClinicalData 96
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.2 Aimsandcontribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.3 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.4 Materialsandmethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
5.4.1 Imagedata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
5.4.2 Thestandardreferenceimage . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.4.3 Automaticbonelocalisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.4.4 Spatio-temporalsegmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.4.5 Automaticrigidbonealignmenttothestandardreferenceimage . . . . . . . . . 102
5.5 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.5.1 Imagedata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.5.2 Automaticbonelocalisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.5.3 Spatio-temporalsegmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.5.4 Automaticrigidbonealignmenttothestandardreferenceimage . . . . . . . . . 109
5.6 Discussionandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
Contents 10
6 UseoftheGridinAutomatedMedicalImageAnalysis 121
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
6.2 Aimsandcontribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
6.3 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
6.4 GridTechnologies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
6.4.1 TheGrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
6.4.2 CondorandCondorMW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
6.4.3 TheVirtualDataGrid(VDG) . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
6.5 Materialsandmethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
6.5.1 Theweb-basedimageanalysisworkbench . . . . . . . . . . . . . . . . . . . . . 128
6.5.2 VDLwrappingoftheimageanalysisalgorithms . . . . . . . . . . . . . . . . . 129
6.5.3 Theparallelimplementationofthespatio-temporalsegmentationalgorithm . . . 132
6.6 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
6.6.1 Theweb-basedimageanalysisworkbench . . . . . . . . . . . . . . . . . . . . . 132
6.6.2 Theparallelimplementationofthespatio-temporalsegmentationalgorithm . . . 135
6.7 Theevaluationoftheworkbench . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
6.8 Discussionandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
7 Conclusions 145
7.1 Summaryoffindings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
7.1.1 AutomaticquantificationofchangesinlongitudinalMRimagesofjointsI . . . . 145
7.1.2 AutomaticquantificationofchangesinlongitudinalMRimagesofjointsII . . . 146
7.1.3 Applicationtoclinicaldata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
7.1.4 UseoftheGridinautomatedmedicalimageanalysis . . . . . . . . . . . . . . . 147
7.2 Futurework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
7.2.1 ImprovementstothequantificationofchangesintheexperimentalmodelofRA. 148
7.2.2 Otherapplicationsofthespatio-temporalsegmentation . . . . . . . . . . . . . . 148
7.2.3 Automaticwindowwidthdeterminationinthespatio-temporalsegmentation . . 149
7.2.4 Improvementstoautomaticbonelocalisationandalignment . . . . . . . . . . . 149
7.2.5 Dynamiccontrastenhanced(DCE)imagingofsynovitisinRApatients . . . . . 152
7.2.6 ImprovementstotheintegrationofmedicalimageanalysisandtheGrid . . . . . 152
7.2.7 Differentwaysofbuildingthefeaturespaceinthespatio-temporalsegmentation 152
7.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
Appendices 153
A AppendixofChapter3 154
A.1 Binaryopeninganddilationoperators . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
A.2 Averagebonelesionvolumesinvariouscandidatebonelesionregions . . . . . . . . . . 154