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GliomaTissueModelingbyCombingtheInformationofMRIandinvivoMultivoxelMRSWeibeiDOU,AoyanDONG,PingCHITsinghuaNationalLaboratoryforInformationScienceandTechnologyDept.ofElectronicEngineering,TsinghuaUniversity,Beijing,100084,P.R.Chinae-maildouwbtsinghua.edu.cnShaowuLINeuroimagingCenterofTiantanHospitalCapitalMedicalUniversity,Beijing,P.R.ChinaJean-MarcCONSTANSUnitd’IRM,EA3916,CHRUCaen,FranceAbstractThispaperpresentsagliomamodelizationmethodandaregression-likemodeltocreateagraduallygliomaimageGlioIm.Multimodalsignal,imagesofmagneticresonanceimagingMRIandinvivomultivoxelMRspectroscopyMRSarecombinedbytheregression-likemodelwithspatialresolutionregistration.ThismodelingmethodconsistsoffeaturemodelsofgliomasuchasthesignalintensityofMRimageandthemetabolitechangesofMRS,thecorrelationmodelnotedasmetabolitesratioMetaRandthecombinedregression-likemodel.TheestimatedGlioImincludesbothbrainstructureandgliomagradeinformation.Anonlinearmodelisproposedandvalidatedinthispaper.ThetestingdataisacquiredbySiemensTrioTim3TandSyngoMRB15atBeijingTiantanhospitalofChina.TheMRSofthreegliomapatients,twoaffectedbyastrocytomaandonebyglioma,andthechemicalshiftimagingCSIreferenceT2imageswereconsideredinourvalidationexperiment.TheresultingGlioImsarecomparedwithgroundtruthprovidedbyneuroradiologistsofTiantanandverifiedwiththeirpathologyreport.Theyreportthatourmethodandmodelareveryefficient.Keywords-MRSpectroscopy;brain;glioma;chemicalshiftimaging;MRI;image;modeling;combinationI.INTRODUCTIONTodiagnosebraintissueabnormalities,liketumor,it’snecessarytousemultispectralmagneticresonanceimagesMRIs,suchasT1-weight,T2-weight,Gadolinium,FLAIRetcinordertofindsomeoftumor’spropertiessuchassize,position,sort,andrelationshipwithothertissues,etc...Butthetumortypeandgradeareusuallydiagnosedfromhistopathologicalexaminationofasurgicalspecimen.However,Hydrogen11HmagneticresonancespectroscopyMRSisanon-invasiveMRtechniquethatprovidesbiochemicalinformationofmetabolites.Themajorbiochemicalcharacteristicscannoninvasivelyprovideusefulinformationonbraintumortypeandgrade[1].Inmanystudies,invivo1H-MRShasbeenpresentedfordeterminingthetypeandgradeoftumors[1][2][3].SinceinvivoMRSmeasurementsandanalysisaredependentontheacquisitiontechnicalthatcompromisethespatialresolutionandaccuracyforresultingmetabolitevalues[4],metabolicchangeswithdiseaseisfrequentlysubtleanddiffuse.Furthermore,bychemical-shiftimagingCSItechnique,themetaboliteimagesso-calledMRspectroscopicimagingMRSIcanbecreatedbymultivoxelMRSinformation,butitisnotvisuallyinterpretableinthesenseofastructuralMRI[4].Sothat,forthetumortissueclassification,itisimportantthatMRSIiscombinedwithMRItoestimatethevariationofmetabolitesandtoyieldmuchinformationregardingtissue.Duringmorethanadecade,automaticbraintumorclassificationbyMRShasbeendeveloped[5],butthemorecleardefinitionofbraintumortypeandgrademaybeobtainedbycombinationofMRSIandMRI[5].AtechniquetodifferentiateglioblastomafrommetastasislesionsbyusingMRIandMRSdatahasbeenpublishedin[6].Wangetal.describedaclassificationofbraintumorsbyusingfeaturesselectionandfuzzyconnectednessin[7],thesefeaturesareextractedfromMRIandMRSdata.TherearetwodifficultiesforcombingMRSIdataandMRIdatafirstly,thesedataarefromdifferentmodalities,sotheyarenotinthesamespatialresolution,verylowspatialresolutioninvoxelforMRSIandhighspatialresolutioninpixelforMRI.Secondly,oneMRimagecorrespondstothedistributionofalltissues,ortissuestructure.ButoneMRSimageisaprojectionimagewhichcorrespondstoonemetaboliteorratiobetweenseveralmetabolites.SothedifferentmetabolitevaluesmakevariationMRSimages,justlikethemappingofmetabolitedistributionsbyMRSIpresentedin[8].ThequestionforapplicationishowtocombinetheseMRSimagesandMRimagestogiveanautomatictissueclassificationresult.ThekeypointofthecombinationishowtomodelthemetabolitedistributionfromMRS,whichcorrespondstoinformationfromMRimages.Forautomaticdescriptionofbraintumortypeandgrade,weproposeamodelizationmethodofgliomatissuesbycombingtheinformation,fromMRimagesandMulitivoxelMRSdata.ItcancreateaMRS-weightedMRimageautomaticallywhichkeepsthehighspatialresolutionlikeMRimageandthegreylevelscorrespondtothedeteriorationofbraintissues.ThesecondpartofthispaperintroducesthegliomatissuefeaturesbothinMRSvaluesandinMRimages.Thecombinationmodelingofthetwotypesofinformationispresentedinthethirdsectionanditsvalidationisshowninthefourthsection.Theconclusionaboutourresearchisgivenattheendofthispaper.ThisworkisfundedbyTsinghuaNationalLaboratoryforInformationScienceandTechnology(TNList)Cross-disciplineFoundation978-1-4244-4713-8/10/25.002010IEEEII.FEATURESMODELOFGLIOMATISSUEFollowingtheresearchofdiagnosingbraintumorbyMRimagesandMRS,wecansummarizetwotypesofcharacteristicsofglioma,oneisthesignalintensityofT1-weightandT2-weightimages,andtheotheroneisthechemical-shiftvaluesofmetabolitespresentedbyMRSdata.A.SignalIntensityCharacteristicsofMRimagesWehaveproposedsomefuzzymodelingmethodsofdifferenttumorouscerebraltissuesonMRimagesbasedonfusionoftissuefeaturesin[9][10][11].TableIdescribesthecharacteristicsofbraintissuesbycreatingagradualityofsignalintensityasafunctionofdifferenttissuesandsequencesofMRI[10],whereCSFistheabbreviationofcerebralspinalfluid,GMtheabbreviationofgraymatter,andWMwhitematter.IntableI,the“Seqs”isshortforSequencesofMRI”.Thesymbol“”presentsahyper-signal;itmeansthatthesignalintensityisveryhighandtheimageisverybright.Thesymbol“-”presentsahypo-signal,theintensityisverylowandtheimageisverydark.Thesymbol“-”meansthatthesignalintensityishigherthanhypo-signal,and“-”meansthatitisdarkerthanhyper-signal.“--”meansthatthesignalintensityislowerthanthehypo-signal,and“”meansthatitisbrighterthanthehyper-signal.AnexampleofT1-weightedimagenotedasT1,andT2-weightedimagenotedasT2areshowninFig.1TABLEI.SIGNALINTENSITYCHARACTERISTICSOFBRAINTISSUESONMRIMAGESSequencesGradualityofsignalintensityCSFGMWMGliomaEdemaNecrosisT1--------------T2---abFigure1.OriginalMRIimagesaT1image,bT2imageB.MetaboliteChangesFeaturesofMRSTABLEII.SCALARDESCRIPTIONOFMETABOLITEVALUESMetabolitelevelabsentverylowlittlelowlowmediumlittlehighhighveryhighabbreviationAVLLLLMLHHVHThereareonlyseveralmetaboliteswhichcorrespondtogliomaamongalargenumberofmetabolitesofhumanbody.N-acetyl-asparateNAA,creatineCr,cholineCho,myo-inositolmI,lactateLacandfreelipidsLip.ThevariationofthesemetabolitescanbeorderedinascalarformasshowninTableII,wherethescalarorderisabsent,verylow,littlelow,low,medium,littlehigh,high,veryhigh,whichcorrespondtometabolitevaluesfrom0tomaximum.ThemetabolicchangeswithbraintissuesareshowninTableIII.Itisconcludedfrom[12][13][14].TABLEIII.METABOLITECHANGESFEATURESOFBRAINTISSUESONMRSMetabolitevariationofmetabolitescorrespondingwithbraintissuesCSFGMWMGliomaEdemaNecrosisNAAVLVHHL/VLMAChoAMLHH/VHLHACrLHHM/LLLAmILMLHHLH/MALipAVLLHLVHLacLHVLAH/LHLHHIII.MODELIZATIONBYCOMBININGMRSWITHMRITheaimofthismodelizationstudyistocreateagraduallygliomaimage,notedasGlioIm,whichincludesbrainstructureandgliomagradeinformation.IfthegliomagradeinformationisconsideredasacorrelationfunctionbetweenMRsignalandpathologicalchanges.Weproposearegression-likemodeltoestimatetheGlioImfromMRimagesnotedasMRImandmetabolitechanges.A.CorrelationmodelOneofthecorrelationfunctionsismetabolitechangescorrespondingtoglioma.BycombiningtheinformationinTableIandTableIII,wecanrebuildaconclusionTableIVaboutgliomacharacteristicswithrelativequantizationofmetabolitesofTableIII.Therelativequantizationisratiosbetweenmetabolitevalues,suchastheratioofChoandNAAnotedasCho/NAAinTableIV,itiscalledmetabolitesratioMetaR,andTableIViscalledcorrelationmodelinthispaper.TABLEIV.METABOLITESRATIOCHARACTERISTICOFBRAINTISSUESMetabolitevariationofmetabolitescorrespondingwithbraintissuesCSFGMWMGliomaEdemaNecrosisCho/NAAAVLLVHHACho/CrALLHHAmI/CrMLMHHALip/CrAVLVLHMVHLac/CrLHVLAHHHTheMetaRcharacteristicsofglioma,edemaandnecrosisareenhancedandthenormaltissuesarereduced.TheyassortwithsignalintensitycharacteristicsofT2-weightedimagedescribedinTableI.B.Regression-likemodelwithspatialresolutionregistrationNormaly,MetaRisafunctionofvoxeldecidedbyCSIsliceshowninFig.2.Sothat,itisatwodimensionalfunctionnotedasMetaRi,v,where“i”isindexofmetaboliteand“v”istheindexofvoxelcorrespondedwithCSIslice.Asthesamereason,GlioImcanbecreatedasathreedimensionalfunction,notedasGlioImv,p,g,where“p”isindexofpixelcorrespondedwithMRIm,and“g”isthegreylevelofselectedMRimageandcorrespondsto“p”.Infact,MRImisatwodimensionalfunctionnotedasMRImp,g,whereandg∈G,{}1,2,,,,,...TTPDFLAIRGadoDiffusionPerfusionGConsidertwovariables,MRImandGlioIm,MRImisacertainimagelikeT2,GlioImisanestimatedimage.ThecorrelationmodelMetaRcanbeconsideredasonerelationshipbetweenthem.Sotheregression-likemodelforestimatingGlioImfromMRImcanbecreatedasequation1.Im,,,Im,GliovpgMetaRivMRpgΘ1Where“Θ”notesanecessaryoperator,and“p”correspondsto“v”.Ifalinearregressiveisnecessary,equation1canberewrittenas2Im,,,Im,,GliovpgMetaRivMRpgMetaRjv2where“i”and“j”indicatedifferentmetabolites.C.NonlinearRegression-likemodelToavoidmosaiceffects,weproposeanonlinearregression-likemodelwithspatialresolutionregistrationin3.Im,Im,,exp,.MRpgGliovpgMetaRivMetaRjvT⎡⎤⎢⎥⎣⎦3where“T”isatimeconstantcorrespondingtoMRImp,g.AccordingtothecorrelationmodelofTableIV,theLip/CrandLac/Crarespecificfeatureswhicharedependentonthetumorgrade.Sothat,inthemodelofequation2,wehave{},/,/,//,/ijMetaRChoNaaChoCrmICrLipCrLacCr∈∈IJIJIJ∪,BecausetheJofMetaRisthegrademarker,ittakesaninterceptiveroletomakeadifferentgreylevelfromothervoxelsandindicatesavariablegrade.IV.VALIDATIONANDRESULTA.MaterielThreegliomapatients,twoaffectedbyastrocytomaandonebyglioma,wereconsideredinourvalidationexperiment.ThetestingdataareadatapairconsistedofCSIrawdataandtheirreferenceimages.ThesedatawereacquiredwithSTEAMsequenceatBeijingTiantanhospitalChina,bySiemensMRTrioTim3TandsyngoMRB15.TheMRSrawdataaremeasuredbycsi_st/90protocolwithTR3000/TE72/TM6.T2-weightedimagesaremeasuredbyt2_tse_traprotocolwithTR4500/TE80.TwoexamplesofthesedataareshowninFig.2.Thenonlinearregression-likemodel3isvalidatedbyourtestingexperimentation.MRImof3isT2with0.570.57mm2pixelsizeand5mmslicethickness.ThetimeconstantTin3isindicatedbyhistogrampeakofCSIreferenceimagesinT2.ThemetabolitevaluesarecalculatedbyTHU-MRSv0.5developedbyourresearchgroupandpublishedin[15].TheCSIslicesnotethattheMRSvoxelsizeis141420mm3.abFigure2.ExampleofCSIslicedown-leftwithitsreferenceimagesandmetabolitesvaluescorrespondedwithvoxelsize141420mm3.afromanastrocytomapatient,masculine30yearsold.bfromagliomapatient,feminie48yearsold.B.ResultThevalidationresultscorrespondedtoVOIareshowninFig.3fandFig.4f.Thehighersignalorbrighterpixelinfmarksgreaterpossibilityofgliomaorhighertumorgrade.InFig.3and4,aaretheoriginalT2-weightedimageswiththesignofVOI,barethehandlabelresultsas“Groundtruth”fromneuroradiologistsofTiantan,careonepartofainVOI,daretheresultsofexponentialcomponentofequation3whichpresentsthecombinedinformationofT2andCho/Naa,earetheresultsofsuperpositionofT2andLipLc/Cr.aT2VOIbGroundtruthcOriginalT2inVOIdMetaRCho/NaaeMetaRLacLip/CrfResultingGlioImFigure3.ResultingGlioImfofthepatientaffectedbyastrocytomaC.DiscussionThebrighterpixelinFig.3dorfdenotesnotonlyhigherCho/NaabutalsobrighterT2.BecauseMetaRvaluesinTableIVareconsistentwiththeintensityofT2.Soitmayindicategliomaandhighergraderegion.ThedarkerpixelspresentlowerCho/NaaanddarkerT2,mayindicatenormaltissues.Thentherearesomebrighterpixelsindandf,theyarenotconnectedwithgliomaregion,theyareCSFperhaps,becauseCSFisbrighterinT2.WecanremovethemsimplybyusingregisteredFLAIRimage.TheregisteredGadoliniumimagealsocanbeusedtoindicateenhancedpixelsorregion.aT2VOIbGroundtruthcoriginalT2inVOIdMetaRCho/NaaeMetaRLacLip/CrfResultingGlioImFigure4.ResultingGlioImfofthepatientaffectedbygliomaBecauseamongthe5metaboliteratiosinTableIV,onlythreepresentevidentchanges,likeCho/Naa,Lip/CrandLac/Cr.Theothertworatiosarenotutilizedinourexperiment.ItispossibletouseotherMRIsequencessuchasT1,butitisnecessaryeithertotransformgreylevelsofimageortoinversethevalueofMetaR.Asmentionedin[16],animageresultedfromfusionofgliomafeaturesextractedfrommultimodalitysignal,aspresentedin[9],canalsobeusedasMRIminthisgliomamodel.V.CONCLUSIONAdvantagesofMRItechniqueprovidemorepossibilitywithmulti-sequencesandmultimodalitiessignaltorealizethetumordiagnosis,treatmentandprognosis.Butitisheavyworkforprocessingallsignalstodoafinaldecision.SoAutomaticquantificationandcombinationanalysisisveryimportantandthemodelingoftumorfeaturesisthekeypointforperformingit.Wehaveproposedaframeworkoffuzzyfeaturesfusionsystemin[16]andpublishedsomeresearchresultsaboutfusingthetumorfeaturesextractedfromT1,T2andprotondensityimages[9].Inthispaper,wepresenttheprimarystudyaboutthetumorfeaturescombinationofMRSandMRimages.Theproposedmodelingmethodandnonlinearregression-likemodelarevalidforseparatingthebraintissuesespeciallyglioma.Itwillbeusedfortumortissuesclassification,segmentation,tumortypeandgradedecision,etc.Thereisstillmuchworktoimprovethismodelandtointegrateitwiththefusionsysteminthefuture.REFERENCES[1]HoweFA,BartonSJ,CudlipSA,StubbsM,SaundersDE,MurphyM,WilkinsP,OpstadKS,DoyleVL,McLeanMA,BellBA,GriffithsJR.“Metabolicprofilesofhumanbraintumorsusingquantitativeinvivo1Hmagneticresonancespectroscopy”.MagnResonMed.2003Feb;492223-32.[2]PreulMC,CaramanosZ,CollinsDL,VillemureJ-G,LeblancR,OlivierA,PokrupaR,ArnoldD.Accurate,non-invasivediagnosisofhumanbraintumorsbyusingprotonmagneticresonancespectroscopy.NatMed1996;2323–325.[3]MajsC,AguileraC,CosM,CaminsA,CandiotaAP,Delgado-GoiT,SamitierA,CastaerS,SnchezJJ,MatoD,AcebesJJ,ArsC.“Invivoprotonmagneticresonancespectroscopyofintraventriculartumoursofthebrain”EurRadiol.2009Aug;1982049-59.[4]A.A.Maudsley,C.Domenig,V.Govind,A.Darkazanli,C.Studholme,K.Arheart,C.Bloomer,“MappingofbrainmetabolitedistributionsbyvolumetricprotonMRspectroscopicimagingMRSI”MagneticResonnanceinMedicin61548-5592009.[5]Garcia-GomezJ.,LutsJ.,Julia-SapeM.,KrooshofP.,TortajadaS.,VicenteJ.,MelssenW.,Fuster-GarciaE.,OlierI.,PostmaG.,MonleonD.,Moreno-TorresA.,PujolJ.,CandiotaA.-P.,Martinez-BisbalM.C.,SuykensJ.A.K.,BuydensL.,CeldaB.,VanHuffelS.,ArusC.,RoblesM.,Multiproject-multicenterevaluationofautomaticbraintumorclassificationbymagneticresonancespectroscopy,MagneticResonanceMaterialsinPhysics,BiologyandMedicine,vol.22,Feb.2009,pp.5-18.[6]LutsJ.,LaudadioT.,Martinez-BisbalM.C.,VanCauterS.,MollaE.,PiquerJ.,SuykensJ.A.K.,HimmelreichU.,CeldaB.,VanHuffelS.,DifferentiationbetweenbrainmetastasesandglioblastomamultiformebasedonMRI,MRSandMRSI 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