COMPUTATIONAL ALGORITHMS FOR RECONSTRUCTION OF CROSSINGWHITE MATTER FIBERS IN BRAIN

Abstract

Magneticresonanceimaging(MRI)isanon-invasiveimagingtechniqueusedto visualizehumantissues.Diffusiontensorimaging(DTI)isaspecificMRImethodfor visualizingbrainwhitemattertracts.However,DTIhaslimitationsindetectingmulti- ple fiberorientations.Toaddressthis,mixturemodelslikethemixtureofGaussiandis- tribution(MoG),mixtureofcentralWishartdistribution(MoCW),andmixtureofnon- centralWishartdistribution(MoNCW)havebeenintroduced.Thesemodelsenable thevoxel-wisemulti-compartmentalization,dividingeachvoxelintomultiplecom- partmentstoaccountforcomplexdiffusionpatternsinthebrain.Toaddresstheun- certaintyinfiberorientations,auniformgradientdirections(UGDs)samplingscheme is used,distributingafixednumberofgradientdirectionsuniformlyonaunitsphere. Thisapproachensuresunbiasedestimationoffiberorientationswithineachvoxel. A largevalueofthesegradientdirectionsisemployedforbetterfiberreconstruction. However,evenwithalargenumberofgradientdirections,accuratelydistinguishing closelyorientedcrossingfiberswithsmallseparationanglesremainschallengingand computationallyintensive,potentiallyleadingtolongercomputationtime.Themain goal ofthisthesisistodevelopcomputationalalgorithmsforaccuratelyreconstruct- ing crossingwhitematterfibershavingsmallseparationangles.Thisiscrucialbecause differentorganizationandarrangementofWMFscanprovideinsightsintobraincon- ditions associatedwithneurologicalabnormalities,psychiatricdisorders,anddevel- opmentalissues. Thethesisintroducesanoveltechniquecalledadaptivegradientdirections(AGDs) for improvingthereconstructionofsingleandcrossingfibers.TheAGDapproachin- volvesatwo-stepalgorithm:usingasmallnumberofuniformlydistributedgradient directionstoaccountforroughfiberorientationinthefirststep,andgeneratingnew gradientdirectionsinproximitytotheobtainedorientationinthesecondstepinagrid likepattern.Aniterativeapproachisalsointroducedforgradientdirectiongeneration. Bothapproachesenhancereconstructionresultsandreduceangularerror.