oxford medicine online principles of brain single-photon emission computed tomography ... · 2018....

Principles of brain single-photon emission computed tomography imaging

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PRINTED FROM OXFORD MEDICINE ONLINE (www.oxfordmedicine.com). ©Oxford University Press, 2015. All RightsReserved. Under the terms of the l icence agreement, an individual user may print out a PDF of a single chapter of a title in OxfordMedicine Online for personal use (for details see Privacy Policy). Subscriber: Habib Zaidi; date: 27 November 2015

Publisher: OxfordUniversityPress PrintPublicationDate: Oct2015PrintISBN-13: 9780199664092 Publishedonline: Oct2015DOI: 10.1093/med/9780199664092.001.0001

Chapter: Principlesofbrainsingle-photonemissioncomputedtomographyimagingAuthor(s): YongDuandHabibZaidiDOI: 10.1093/med/9780199664092.003.0008

OxfordMedicineOnline

OxfordTextbookofNeuroimagingEditedbyMassimoFilippi

Principlesofbrainsingle-photonemissioncomputedtomographyimaging

IntroductionThehistoricaldevelopmentofmedicalimagingismarkedbynumeroussignificanttechnologicalaccomplishments,drivenbyanunprecedentedcollaborationbetweenmultidisciplinaryresearchgroups.Thefirstmedicalapplicationsoftomographicimagingfocusedonthebrain[1].X-raycomputedtomography(CT)andmagneticresonanceimaging(MRI)have,foralongtime,beenthemostwidely-usedimagingmodalitiesforanatomicassessmentofpathologicprocessesaffectingthebrain.Alternatively,radionuclideimagingmethods,includingsingle-photonemissioncomputedtomography(SPECT)andpositronemissiontomography(PET)haveemergedasusefulmedicalimagingtechnologiesforevaluatingbrainfunction.Thedevelopmentoftheformertechnologyforbrainimagingdatesbackto1976whenDrsKeyesandJaszczakreportedindependentlyonthedevelopmentofabrainSPECTsystembasedonAngercameramountedonarotatinggantry[2,3].Asnuclearmedicineimaginghasbecomeintegratedintoclinicalpractice,severaldesigntrendshavedeveloped;withsystemsnowavailablewithaspectrumoffeatures,fromthosedesignedfor


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clinicalwhole-bodyapplicationstoothersdesignedspecificallyforveryhigh-resolutionbrainresearchapplications[4].

SPECTisanon-invasive3Dtomographicimagingmodalitythatiswidelyusedinbothclinicaldiagnosisandacademicresearchtoassessmanydiseases[5,6,7].InSPECTimaging,the3Ddistributionofaradionuclide-labelledagentadministeredtoapatientismeasuredbydetectingthegamma-photonsemittedfromthedecayoftheradioactiveisotopesattachedtotheradiotracer.BecauseSPECTagentscanbedesignedtotargetspecificphysiologicalfunctions,SPECTimagescanprovideinformationonphysiologicalandphysiopathologicalprocessesatamolecularlevel.Thisuniquefeaturemakesitausefultoolforinvivoimagingofhumanbrainfunction,especiallyforstudyingdysfunctionoftheneurotransmissionsystemthatisrelatedtomanybraindiseases[8].Asaresult,brainSPECTimagingbecameapowerfultoolfordiagnosis,prognosis,evaluationofresponsetotherapy,andchoiceoftreatmentplanformanyneurodegenerativediseases[9,10].

Brainsingle-photonemissioncomputedtomographyinstrumentationConventionalSPECTimagingsystems

SincethepurposeofSPECTimagingistomeasureandrepresentthe3Ddistributionoftheradiopharmaceuticalsadministeredtothepatient,theenergiesofgammarayphotonsemittedbylabellingradionuclidesarehighenoughtoallowpenetrationthroughthepatient’sbody.Theemittedphotonscanbedetectedusingtheso-calledgammacamera(Fig.8.1)togenerateatwo-dimensional(2D)snapshotplanarimage.The2Dplanarimageisreferredtoasprojectionimagesinceitrepresentsaprojectedviewofthethree-dimensional(3D)radiotracerdistributionatacertainangle.Toacquirea3Ddistribution,aseriesofprojectionimagesaretakenaroundthepatientatdifferentdiscreteanglesbyrotatingthegammacameraaroundthepatientalongthesuperior-inferioraxis.Afteracquisition,the3Ddistributioncanthenbereconstructedfromthemeasuredprojectionimagesusingoneoftheavailablereconstructiontechniques.

Fig.8.1SchematicrepresentationofaSPECTgammacameraanditsmaincomponents.

Usually,conventionalSPECTimagingsystemshavetwoorthreegammacamerasthataremountedonagantrywhichprovidesmechanicalsupportandrotationofthecameras.Themaincomponentsofagammacameraincludethecollimator,scintillationcrystal,andphotomultipliertubes(PMTs)(Fig.8.1).

Thecollimatorisusedtoselectivelydetectphotonstravellinginacertaindirectionbecausethisinformationisneededforimagereconstruction.Itismadeofhighlydensephotonabsorbing


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material,usuallyleadortungsten,withapatternofholesthatallowsonlyphotonstravellingparalleltothehole-directiontopassandreachthedetectorcrystal.Theholescanbearrangedwiththesamedirection(parallelholecollimator)orconvergetocertainpoints(forexample,aconebeamcollimator).Thescintillatorcrystal,commonlysodiumiodide(NaI)crystal,willabsorbthephotonandtransferitsenergyintovisibleorultraviolet(UV)lightphotonsthroughaprocesscalledscintillation.ThenumberofvisibleorUVphotonsisproportionaltotheenergyofincidentphotons.Thevisible/UVphotonswilltraveltothePMTtroughalightguideandproduceanelectriccurrentsignal.ThePMTcanmultiplythecurrentsignalbyafactorofasmuchasamillion.Thesignalwillthenbefurtheramplifiedthroughtheamplifierandsenttoprocessingelectronicstogeneratethepositionandtheenergyinformationoftheincidentgammaphoton.Theinformationwillthenbepassedontoanacquisitionboard/computertogeneratetheprojectionimages.

ManyoftheclinicalSPECTsystemscommerciallyavailabletodayaredual-modalitySPECT/CTsystemsthatalsoincludeaCTsubsystem[11].TheSPECTimagesandCTimagesarecomplimentarytoeachother:SPECTimagesprovidefunctionalinformation,whiletheCTimagesprovideanatomicalinformationaboutlocationofdiseasesite.Byfusingthetwoimagestogether,anaccurateinterpretationofimagescanbeachieved.Inaddition,CTimagescanalsobeconvertedintoattenuationmapsrequiredforattenuationcompensationtoimproveSPECTimagequalityandquantitativeaccuracy.AplethoraofCTscannersareusedoncombinedSPECT/CTsystems.SomeusediagnosticqualityCTtoprovideimagessuitableforclinicalusage.Toreducepatientradiationdose,low-doseCTscannershavealsobeendeployed,mostlyfortypicalnuclearmedicineapplications[12].Theimagequalityoflow-doseCTisusuallylowandcanonlybeusedforattenuationcompensationandlocalization.However,withtheadvanceofiterativeCTreconstructionalgorithms,low-doseCTscannowalsoprovideimagesofgoodquality[13].

Dedicatedcollimatorsandhardwareforbrainimaging

MostclinicalSPECTsystemscanbeusedforvariousapplicationsthroughappropriateselectionofthemostsuitedcollimatorforaparticularstudy.ThecollimatoristhemostimportantpartofaSPECTcameraandisconsideredtobethemaincomponentaffectingspatialresolution,sensitivity,andimagenoise.Thechoiceofthecollimatordependsontheimagedobjectandtrade-offconsiderationsbetweensensitivityandspatialresolution.Usually,collimatorswithbetterspatialresolutionhavelowersensitivity,thusrequiringalongacquisitiontimeasotherwisetheimagewillbeverynoisy.Ontheotherhand,collimatorswithhighersensitivitywillprovideimageswithlowerresolution.Themostcommonlyusedcollimatorsareparallel-holecollimators.Forexample,inbrainimagingusing Tc-labelledcompounds,alow-energyhigh-resolution(LEHR)parallel-holecollimatorisoftenusedtoprovideimageswithhighspatialresolution.BecausehighspatialresolutionisdesirableinbrainSPECTimaging,muchworthwhileresearchfocusedondesigningdedicatedcollimatorstoimprovespatialresolutionwithoutsacrificingsensitivity[4].

Mostofthesecollimatorsareconvergingcollimators,inwhichtheholesarefocusedtoaline(fanbeamcollimator)orapoint(conebeamcollimator)infrontofthecollimator.Anumberofvaryingfocallengthcollimators,wheretheholesarefocusedtodifferentpointsinspacehavealsobeendevelopedandevaluatedinclinicalsetting.Thedetectionefficiencyofconvergingbeamcollimatorsishigherthanthatofparallel-holecollimators.Thespatialresolutionisalsoslightlybetter.Pinholecollimatorshavealsobeendesignedforimagingsmallorgans,suchasthethyroidortheextremitiestoprovideabetterspatialresolution,butattheexpenseofdetectionefficiency.Theefficiencycanbesubstantiallyimprovedbyusingmultiplepinholes[14].

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Furthermore,eveninthepresenceofhighcountstatistics,themechanicalmotionsinvolvedandthebulkofthedetectorheadslimittheshortesttimeinwhichacompletesetofprojectionscanberecorded[15,16].NumerousattemptshavebeencarriedouttoconsiderablyboostbrainSPECTsensitivityusingseveraldetectorheadsorring-likearrangementofdetectorstoenabletheveryrapidacquisitionofafullsetofprojections.AlthoughtheuseofsuchdedicatedsystemsisnotprevalentintheclinicforbrainSPECTimaging,theyareusefulforresearchapplicationsandcouldalsobeusefulfortheassessmentofthepotentialroleofadvancesininstrumentationinthefuture[17,18].AgoodexampleoffullringdetectorsystemsistheFASTSPECT,developedattheUniversityofArizona,whichconsistsof24position-sensitiveNaI(Tl)detectorsthatarecompletelystationary,togetherwithastationarysetof2-mmpinholecollimators,hence,achievinghighsensitivity(fastdynamicbrainscans)andhighspatialresolution[19].Theringdetectorsfromstationarysystemssurroundthehead,whichmakesitpossibletoacquiredatafrom360°atthesametime.Thisisespeciallyattractivefordynamicimagingwhereashortrepeat-scanoveraperiodoftimeisperformed.AnotherdedicatedbrainscannerwithstationaryannularNaI(Tl)detectoristheCERASPECTsystem,developedbyDigitalScintigraphicsInc.[20],whichisequippedwitharotatingcollimator.AmodifiedversionofthiscollimatoristheSensOgrade,avariablefocusingcollimator,whichsamplestheprojectionsunequally,withcentralregionsmoreheavilyrepresentedtocompensateforattenuationfromcentralbrainstructures,thusyieldingafour-tonine-foldincreaseinsensitivitycomparedwithconventionaldual-headcameras[21].AthirdexampleistheSPRINTIIsystemdevelopedattheUniversityofMichigan,whichconsistsof11detectorsarrangedinapolygonalfashionandarotatingcollimator,whichallowstheacquisitionofacompletesetoffan-beamprojectiondatawithin1/12ofarotation[22].AnotherexampleofunconventionalsystemsistheNeuroFocus™multi-conebeamimager(NeurophysicsCorporation,Shirley,MA,USA),whichproducestomographicSPECTimageswithanintrinsicspatialresolutionof~3mm.TheoperationoftheNeuroFocus™high-definitionfocusingemissiontomographicscanner(HDFET)followsthesameprinciplesofscanningopticalmicroscopestoobtainhigh-resolution3Dimagesofbiologicaltissue[23].Ahighlyfocusedpointoflightisscannedmechanicallyinthree-dimensionstouniformlysamplethevolumeunderobservation.AsanalternativetodynamicSPECTimagingusingmultiple,fastrotations,strategiesinvolvingtheuseofonlyasingle,slowcamerarotationhavebeenproposed[24,25].Furtheradvancesinelectronicsarepermittingnewcountingstrategiesandadvancesinelectroniccomponentcapabilityareallowingforenhancedsensitivity[26].

DataacquisitionprotocolsDataacquisition

DuringSPECTimageacquisition,aseriesof2Dprojectionimagesaretakenatdifferentanglesbyrotatingthecameraaroundthepatient.Toacquireenoughdataforimagereconstruction,itisimportanttoselectthecorrectscanarc,whichdefinestherangeofrotationandwheretostart.Forexample,forcardiacimaginga180°rotationfromrightposteriorobliquetoleftanteriorobliqueisused,whileforbrainimaginga360°rotationisusuallyrequired.Onealsoneedstodecidetheorbittospecifythedistanceofthecamerafromthepatientateachangle.Theorbitcanbeeithercircularornon-circular.Non-circularmeansthatthedistancesofthecamerafromthecentreofrotation,termedastheradiusofrotation,arenotthesamefordifferentangles.Inacircularorbit,theradiusofrotationisconstant.Foraparallel-holecollimator,thecloserthepatienttothecamera,thebetterthespatialresolutionis.Therefore,anon-circularorbitisoftenusedforimagingthetorso,wherethecameraispositionedasclosetothepatientaspossible.InmodernSPECT


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systems,thiscanbedoneautomaticallyusingopticalsensorsinstalledonthecamera.Forbrainimaging,duetotheroundshapeofthehead,acircularorbitisoftenusedwheretheoperatorcanspecifytheradiusofrotation.

Thegammaphotonsemittedfromradioactivedecayhavecharacteristicenergy/energies,calledpeaksthatarespecifictotheisotope.Forexample, Tcdecayemitsgammaphotonsat140.5keVonly,while Idecayemitsgammaradiationmainlyat159keVwithsomeotherhigherenergypeakswithlowabundance.Ideally,duringSPECTimagingonlyphotonsdetectedwiththeexactpeakenergywillbecountedastrueevents.However,duetothelimitedenergyresolutionofthegammacamera,theenergiesofdetectedphotonsareGaussiandistributedaroundthepeakenergy.Tomaintainareasonablesensitivity,photonsdetectedwithenergiesinacertainrangearoundthepeakenergywillallbecountedastrueevents.Thisenergyrangeiscalledtheacquisitionenergywindow.Ontheotherhand,photonsscatteredinthepatientbodywillcontaminatetheimagesandtheyusuallyhaveabroadenergydistributionlowerthanthepeak.Toreducethenumberofrecordedscatteredphotons,theenergywindowshouldnotbetoowide.Thechoiceoftheenergywindowisdictatedbyabalancebetweenrecordingasmanypeakphotonsaspossiblewithoutsignificantlyincreasingscatteredphotoncounts.Commonly-usedenergieswindowsareusually15–20%wideandcentredaroundthepeakenergy.

AnotherimportantparameterinSPECTistheacquisitiontimeforeachprojectionangle.Longacquisitiontimesresultinhighcountsandlowimagenoise.However,thiswillalsoreducepatientcomfortandincreasechancesofartefactscausedbypatientmovementduringacquisition.Overall,thechoiceofscanningparametersdependsontheimagingtaskathand.Theaimistoacquireasmuchcompletedataaspossibletoprovidegoodimagequalityandmaketheprocesscomfortabletopatients.MostcommercialSPECTsystemshavemanufacturerpresetorrecommendedacquisitionprotocolsforcommonapplications,suchascardiacimaging,brainimaging,tumourimaging,andbonescans.Newprotocolscanalsobesetbytheuserstoaccomplishtheirspecialneeds.Thisrequirestheavailabilityofaqualifiedmedicalphysicisthavingtherequiredskills,expertise,andknowledgeofthistechnology.

Dual-isotopeimaging

Insomesituations,itmaybenecessarytoacquireimagesofmorethanonephysiologicalfunctiontoprovideaccuratediagnosis.Forexample,inbrainimagingofthedopaminergicsystem,itisgenerallyrecognizedthattheanalysisoftheintegrityofboththepre-andthepost-synapticneuroniscrucialindistinguishingdifferentparkinsoniansyndromes.Thepresynapticneuronscanbeimagedwithanagenttargetingthedopaminetransporter(DAT)onthecellmembrane.Agentstargetingthedopaminereceptors,especiallytypeIIreceptor(D2R),couldbeusedinimagingthepost-synapticneurons[27,28].Traditionally,DATandD2RimagingareperformedondifferentdaysbecausemostofDATandD2Ragentsarelabelledwiththesameisotope( I).Sincethehalf-lifeof Idecayis13hours,thetwoscansmustbeseparatedbyseveraldaystoallowtheradioactivityfromthefirstscantodecayinordertoavoidcontaminationofthesecondscan.Duringthisinterval,thepatient’sphysiologicalstatusmayhavechanged.Moreover,thepatient’spositionduringthetwoscanswillprobablybedifferent,whichwillcauseregistrationerrorsbetweentheimages[29].Thisisparticularlyrelevantforpatientssufferingtremors,wheretheartefactsinthetwoimagesmayhavedifferentpatternsthatwillfurthercomplicatediagnosis.

Thesedrawbackscan,however,beovercomebyusingsimultaneousdual-isotopeacquisition

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methods.Takingadvantageofmoderncameras’higherenergyresolutionandtheabilitytoacquiremultipleenergywindows,simultaneousdual-isotopeimagingisamethodthatcanbeusedtodiagnosediseasesbyimagingdifferentphysiologicalinformationrevealedbyagentslabelledwithdifferentisotopesthatemitgammaphotonsatdifferentenergies.Comparedtoseparateimaging,simultaneousacquisitionreducesacquisitiontime,patientdiscomfortandmotionartefacts.Perhaps,moreimportantly,simultaneousacquisitionensuresperfectregistrationoftheimagesfrombothisotopesintimeandspace.Furthermore,motionartefacts,ifany,willbethesameonimagesofbothisotopes.Forexample,withtheintroductionof Tc-labelledDATligands,suchas Tc-TRODAT,SPECTimagingoftheDATandD2Rcannowbecarriedoutsimultaneouslyusingdual-isotope Tc/ Iimaging.Thereisalsointerestinsimultaneousimagingofbrainperfusionusing

Tc-HMPAOandneurotransmissionwith I-IBZM.However,researchonsimultaneousTc/ Idual-isotopebrainimaginghasshownthatimagequalityissignificantlydegradedbythe

cross-talkcontaminationbetweenthetwoisotopes.Cross-talkherereferstothecontaminationofoneagent/isotope’simagecausedbytheotherisotopeusedinsimultaneousdual-isotopeimaging.Beforesimultaneousimagingcanbeadoptedclinically,anefficientcross-talkcompensationmethodmustbedevelopedandvalidated[30,31,32].

ImagereconstructionImagereconstructiontechniques

SPECTimageformationcanbeexpressedasanintegrationofthe3Dradioactivitydistributionalongeachprojectiondirection,i.e.lineofintegration,intoaseriesof2Ddata.Imagereconstructionisaninverseproblemthattriestoestimatethe3Dactivitydistributionfromthe2Dprojectiondata.Bothanalyticalanditerativereconstructionapproacheshavebeendevisedtosolvethisreconstructionprobleminordertoprovidethebestestimatesthatareasclosetothetruthaspossible.Thesetwocategoriesofreconstructionstrategiesarebrieflysummarized.

Analyticalreconstruction

Asdescribedabove,aprojectionisanintegrationoftheactivitydistributionalongthelineofintegration.ThecollectionoftheseprojectionsasafunctionofprojectionangleisreferredtoastheRadontransformoftheobject[33].Byinversingtheradontransformoftheprojections,theoriginal3Dactivitydistributioncanthenbereconstructedanalytically.Themostwidelyusedanalyticalreconstructionmethodisthefilteredback-projection(FBP)algorithm,whichisstillusedinmanyimagereconstructiontasks.

Analyticalreconstructionmethods,suchasFBP,focusonthegeometryandsimplifythephysics.Theyaresimple,fast,andcanprovideaccurateresultswhentheassumedprojectionmodelmatchestheimageformationprocess.However,theyaresensitivetonoiseandmissingdata,whichwillresultinartefactsandlossofreconstructedimageresolution.Anothermajordisadvantageofanalyticalmethodsisthattheydonotallowprecisemodellingofthephysicalandstatisticalcharacteristicsofthedataacquisitionprocess,thusresultinginimageartefactsandpoorquantitation.Thisproblemcan,however,besolvedthroughtheuseofiterativereconstructionalgorithms,whereimagedegradingfactorscanbemodelledduringthereconstructionasdescribedinthenextsection.

Iterativereconstruction

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Fig.8.2showsageneralizedflowchartofaniterativereconstructionalgorithm.Aniterativemethodcanberegardedasanoperatorworkingbetweentheimagespace,i.e.the3Dspacerepresentingtheactivitydistributionintheobject,andprojectionspace,whichrepresentstheSPECTmeasurementofthe2Dprojections.Inaniterativemethod,aninitialestimateisprojectedtogenerateanestimatedprojectionofthecurrentestimate.Estimatedprojectionsarethencomparedwiththemeasuredprojections.Theerrorbetweenthetwoisback-projectedtotheimagespace,thenusedtoupdatethecurrentestimate.Byrepeatingtheprojection–back-projectionprocessinafeedbacklooptoupdatetheimageestimateuntilagivencriterionisfulfilled,iterativemethodscanproducehigh-qualityimageswithimprovedresolutionandnoiseproperties,andresultinadequatequantitativeaccuracy.

Fig.8.2Flowchartofaniterativereconstructionalgorithm.

Amongalliterativereconstructionalgorithms,themostsuccessfulandpopularonesarestatisticalreconstructions,whichareoftensimplyreferredtoasiterativereconstructionapproaches.Aniterativestatisticalreconstructionmethodconsistsofthreemajorcomponents:

1.Anunderlyingstatisticalmodelwithassociatedobjectivefunction.2.Aniterativealgorithmtofindtheoptimalestimateintermsoftheobjectivefunction.3.Amodeloftheimageformationprocessoftenimplementedusingaprojector–back-projectorpair.

Amongthese,theprojector–back-projectorpairisveryimportant.Itservesasabridgeconnectingtheimagespace(estimate)andtheprojectionspace(measurement).Itisintheprojection–back-projectionoperatorsthattheexactmodellingofthephysicsandimageformationprocessisperformed.Throughtheiterativeprocess,themodellingallowsforthecompensationofimage-degradingfactors,suchasscatterandattenuation.Theunderlyingstatisticalmodelandresultingobjectivefunctionplaytheroleofdecidinghowwelltheestimatematchestheprojections,takingintoaccountthenoisepropertiesofthemeasureddataandknowledgeaboutthecharacteristicsoftheunderlyingactivitydistribution.

ThemostpopulariterativereconstructionalgorithminSPECTisthemaximum-likelihoodexpectation-maximization(ML-EM)algorithmanditsacceleratedvariation,theordered-subsetsexpectation-maximization(OS-EM)approach[34,35].TheML-EMalgorithmisbasedonthefactthatphotonemissionanddetectionfromradioactivedecayarePoissondistributed.Itattemptstomaximizethestatisticallikelihoodthatthemeasuredprojectionscamefromthereconstructed


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image.InOS-EM,thealgorithmismadefasterbyusingonlypartoftheprojectiondata,calledasubset,duringeachupdate.ComparedwithML-EM,OS-EMconvergesfasterwithafactorapproximatelyequaltothenumberofsubsetsused.ThiscategoryofreconstructionstrategieswassosuccessfulthatmostcurrentSPECTsystemsoffersoftwareforenduserstoperformML-EMandOS-EMreconstructionwithvariousphysicsmodellingoptions.Whilethishasbeenshowntobeofgreatpracticalvalue,itshouldbenotedthatOS-EMhasarelativelyweaktheoreticalbasisanditsconvergencepropertiesareonlypoorlyunderstood.

Despitetheiradvantagesandattractiveproperties,thesetechnicaladvanceshavemanylimitations.Iterativemethodscanbeverycomputationallydemanding,particularlywhencomplexmodelsareusedtomodelthephysicsoftheimageformationprocess.However,thewidespreadavailabilityofhighperformancecomputing,evenondesktopcomputers(includinggraphicsprocessingunitandcloudcomputing),andthecontinuingdevelopmentofrapidlyconvergingalgorithmshasledtorenewedinterestiniterativetechniquesandmadethemahottopicforleadingmanufacturersandacademicresearchgroups.

Imagedegradingfactors

Ideally,onlyphotonswithanoriginalpaththatisalongthecollimatorholedirectioncanbedetectedintheprojections.However,whengamma-photonstravelinsideanobject,theyinteractwithmatterthroughanumberofphysicalprocesses,suchasphotoelectricabsorptionandComptonscatter[36].Theprobabilityofoccurrenceofeachprocessdependsontheenergyofthephotonandtheelectrondensityofthematerial.Thoseinteractionscouldreducethenumberofphotonsorchangeaphoton’spathandenergy.Thedetectionofthosealternatedphotonscancausefalseinformationanddegradetheimagequalityinmultipleways.Fig.8.3demonstratesthedifferentfactorsthatcandegradeSPECTimages.Amongthose,themostsignificantfactorsarephotonattenuation,scatter,andcollimator-detectorresponse.

Fig.8.3OverviewofphotondetectioninSPECTrepresentingdifferentphysicaldegradingfactors.(1and2)Photonspassthroughthecollimatorwithoutinteractions,1alsoshowstheattenuationofthephotonintensity.(3)Photonpenetratesorscattersinthecollimator-detectorsystem.(4)Photonscattersinsidetheimagingobject.(5)Photoelectricabsorption;and(6)Photonescapestheobjectwithouthittingthecollimator-detectorsystem.

Whenphotonspassthroughtheimagingobject,theycanbeabsorbedbyphotoelectricabsorption


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ordivertedbyComptonscatter.Asaresult,theintensityofthephotonfluxfinallyreachingthedetectorwillbemuchsmallercomparedwiththeoriginaloneandthiseffectisreferredtoasphotonattenuation.Attenuationreducesthemeasuredprojectioncounts.Thedegreedependsonthephotonpathandtheobject.Itcanbeexpressedasanexponentfunctionoftheproductoftheattenuationcoefficientoftheobjectandthedistancethephotonhastotravelintheobject.Ifnotcompensatedfor,attenuationleadstoreconstructedimageswithreducedintensityinvoxelsdeepintheobject.Itcanalsoresultinvisibleartefactsandquantificationwithsignificanterrors.

Anumberofphotonsemittedfromtheradionuclidewillscatterinthepatientatleastoncebeforeexitingthebody.ThedominantscattereventsareComptonscatter,whichreflectsacollisioninteractionbetweenagamma-photonandanoutershellelectronofanatom.Duringthecollision,gamma-photonswilltransfersomeoftheirenergytotheelectronandchangethedirectionofmovement.Theenergyofthescatteredphotonisafunctionofthescatteringangle,givenbytheComptonformula.Thephotonlosesmostofitsenergywhenitisscatteredbackward(180°).Whenthescatteringangledecreases,theenergyofthescatteredphotoncomesclosetotheincomingphoton’senergy.TheprobabilityofComptonscatteringdependsonthephotonenergyandthenumberofelectronsavailableintheobject.Theprobabilityoftheangulardistributionofthescatteredphoton,describedbyKlein–Nishinaformula,isalsoafunctionofthephotonenergyandscatteringangle[36].

BecauseComptonscatteringreducesphotonenergy,usuallyonlythosephotonsscatteredonceortwice,andwithinasmallscatteringanglecanbedetectedwithintheimagingenergywindow.Sincescatterdivertsthedirectionofscatteredphotons,whenthosephotonsaredetected,theyresultincountsinvoxelsthattheywouldnotreachwithoutbeingscattered.Scatterinprojectiondataisspatiallyvaryingandisafunctionoftheobjectattenuationproperties,theimagingenergywindow,andthesourcedistribution.Inbrainimagingusing I-or Tc-labelledcompounds,scatteredphotonscancontributeupto11%ofthetotalcountsinthefinalimages.Ifnotcompensatedfor,scattercouldresultinastructuredbackgroundartefactthatresemblesthemedium-andlow-frequencycharacteroftheactivitydistribution,andassuchreducesimagecontrast.Quantitatively,scattercanleadtooverestimationoftraceruptake[37,38].

Thecollimatoristhecrucialelementdeterminingthesensitivity,spatialresolutionandcontrastofSPECTimages.Aperfectcollimatoronlyallowsthedetectionofphotonsthattravelinadirectionparalleltothecollimatorhole.AsshowninFigs8.3and8.4,sincearealcollimatorholehasafinitegeometricsize,photonsincidentwithinasmallacceptanceanglefromthehole-directioncanstillbedetected.Thisisreferredtoasgeometrically-collimatedphotonsorcollimatorgeometricresponse.Inaddition,duetostatisticvariations,thepositionofphotonabsorptioninthecrystalisusuallydeterminedwithimprecision,andisdefinedasintrinsicresolution.Bothcollimatorgeometricresponseandintrinsicresolutionreducethespatialresolutionandcauseblurringoftheimage.Theseeffectscanbemodelledusinggeometricresponsefunctions.Typically,Gaussianfunctionsareusedtoanalyticallymodelthegeometricresponses.ThefullwidthathalfmaximumoftheGaussianis,therefore,oftenusedasanindicationofthesystemspatialresolution[39].Ingeneral,collimatorswithlargeholesallowmorephotonstopassthrough,butwillalsocausemorespatialblurring.Collimatorswithsmallholescanprovidebetterspatialresolution,butlowersensitivity.Theresolutionisalsoafunctionofdistance:thefarthertheobjectfromthecollimator,theworsetheresolution.

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Fig.8.4Differentcomponentsofthecollimator-detectorresponse.(1)Geometricresponse.(2)Collimatorpenetration.(3)Collimatorscatter.

Photonsincidentonthecollimatorwithlargeanglesareusuallyabsorbedbytheseptabetweencollimatorholes.However,somehigh-energyphotonsemittedfromisotopessuchas IcanstillpenetratetheseptaorpassthroughafterscatteringinsidetheseptaasshowninFig.8.4.Thepenetratedphotonsfromapointsourceusuallyresultinastar-orstripe-shapedpatternintheprojection.Collimatorscatteredphotonsusuallyresultinabroadlydistributedbackgroundintheprojection.Ifnotcompensatedfor,thesephotonscancauseartefactsinthereconstructedimages.Thefullcollimator-detectorresponse,includinggeometricresponse,collimatorpenetration,andcollimatorscatterdependsonthecollimatorgeometryandthephotonenergy.Itcanbemodelledbythecollimator-detectorresponsefunction(CDRF)thatismeasuredorsimulatedbyplacingapointsourceatvariousdistancesfromthefaceofthecollimator.

Fig.8.5showssimulatedSPECTimagesconsideringthedifferentimagedegradingfactorsdiscussedabove.TheimageswerereconstructedusingFBPwithoutpost-reconstructionfiltering.Fromlefttoright,theimagequalityisgettingworsebecausemoredegradingfactorswereincludedinthesimulationprocess.Theworstimageisthelastoneontheright,whichincludesallthephysicaldegradingfactorsandisthemostrepresentativeofwhatisfoundintheclinicwithoutanycompensation.Theimagesalsoindicatethatattenuationreducestheintensityatthecentreofthebrain,collimator-detectorresponseblursimagesandreducesspatialresolution,andscatterreducesimagecontrast.

Fig.8.5

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Effectsofimagedegradingfactors.Theimagesrepresenttheactualtracerdistributioninthestriatalbrainphantom(a)andreconstructedSPECTimagesgeneratedwithoutdegradingfactors(b);withphotonattenuation(c);withattenuationandcollimator-detectorresponseblurring(d);andwithattenuation,collimator-detectorresponseblurring,andscatter(e).

ThespatialresolutionintypicalSPECTimagesisaround1–2cm.AsshowninFig.8.5,thefinitespatialresolutionresultsinblurredimageswithpartialvolumeeffects(PVEs).PVEsincludebothalossofcountsinstructuressmallerthantwoorthreetimesthesystemresolution’sfullwidthathalfmaximum(partialvolume)andcontaminationbetweenadjacentregions(spillover).PVEsalsodependontheshape,size,andrelativeactivitiesoftheobjectsofinterest.Theproceduresappliedafterimageacquisition,suchasimagereconstruction,willalsoaffectthelevelofPVEs.Inbrainimaging,duetothefinedimensionsofthestructuresofinterest,suchastheputamenandcaudate,PVEscancauselargequantitativeerrors.

Inaddition,SPECTimagesalsosufferfromnoise.NoiseinSPECTimagesconsistsmainlyofstatisticalnoiseresultingfromtherandomnatureofphotonemission(followingradioactivedecay)anddetectionprocesses.ItcanbecharacterizedbyaPoissondistributioninwhichthevarianceequalsthemean.Therefore,noiseinSPECTdataisafunctionofthenumberofdetectedcounts—thehigherthecounts,thelesserthenoise.Noisecouldsignificantlyaffectimagequalityandquantitativeaccuracy,andreducethedetectabilityofsubtleabnormalitiesandtheirmeasurement.Noisecanalsocauseimageartefacts,introducingtexturesintouniformly-distributedregions.Increasingthecountlevelusinglongacquisitiontimesorhighinjectiondosescanreducenoise.Lowpassfilteringisoftenusedtosmoothoutnoiseinthereconstructedimages.Alternatively,constrainscanalsobeappliedduringiterativereconstructiontoreducenoise.

Cross-talkindual-isotopeimaging

Insimultaneousdual-isotopeimaging,aphotonemittedfromoneisotopecanbedetectedinanotherisotope’senergywindowowingtothefiniteenergyresolutionofthedetectorsystem,andtheinteractionofphotonswiththeobjectandcollimator-detectorsystem.Thesephotonscanreduceimagequality,degradequantitativeaccuracy,andcauseartefactsintheimages.Thiseffectisreferredtoascross-talkcontamination.Cross-talkcontaminationdependsontheemissionenergiesandtheimagingenergywindowsofbothisotopes.Assuch,theoverallimpactisdifferentforvariousisotopecombinations,makingitdifficulttofindauniversalcompensationmethod.

Fig.8.6showssimulatedenergyspectraofasimultaneous Tc/ Idual-isotopebrainSPECTstudy.Thespectraindicatethatcross-talkfrom Tcinto Iprojectiondataoriginatesmainlyfromunscattered Tcphotonsthataredetectedinthe Ienergywindowasaresultofthedetector’sfiniteenergyresolution.Thecross-talkfrom Iinto Tcwindow,however,iscomplexandincludesphotonsoriginatingfromavarietyofprocesses.Inadditiontothe159keVphotonsusedforimaging, Idecayalsoemitshigh-energyphotonswithenergiesrangingfrom182keVto783keVwithatotalabundanceof~3%[40].Thedown-scatterofthesephotonsintoboth TcandIenergywindowscanfurthercontaminatetheimagesofbothradionuclides.

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Fig.8.6Simulatedenergyspectraofa Tc/ Idual-isotopestudy.

Compensationtechniques

Toimproveimagequalityandquantitativeaccuracy,imagingdegradingfactorshavetobecompensatedfor.Tocompensateforattenuation,anattenuationmaprepresentingthespatialdistributionofthepatient’sattenuationcoefficientsisrequired.TheattenuationmapcanbederivedbyscanningthepatientusingeithertransmissionsourcesofradionuclideshavinganenergyclosetotheenergyoftheradionuclideusedforSPECTimagingorfromCTimagesacquiredoncombinedSPECT-CTsystems.Inthepast,analyticalmethodshavebeenusedtocompensateforphotonattenuation,whichusuallyinvolvesscalingtheprojectionimageorpost-reconstructionfiltering[41,42].Becausethesemethodsoftenassumeuniformattenuationofthemedium,theycanonlyprovideanapproximatecompensation,andsometimesmayevencausemoreartefactswhentheobjectisnon-homogeneous[43].

Accurateattenuationcompensationcanbeachievedusingiterativereconstruction-basedcompensation,wheretheattenuationmapacquiredfromatransmissionscanisusedtomodeltheattenuationintheprojection–back-projectionoperators[44].Iterativereconstruction-basedattenuationcompensationhasbeenshowntosignificantlyimprovetheimagequalityandquantitativeaccuracy,andiscurrentlywidelyusedintheclinic.Similarly,anaccuratecompensationofthecollimator-detectorresponseisusuallyachievedthroughiterativereconstruction-basedcompensationbyincludingtheCDRFmodelsintheprojection–back-projectionoperators[45].

Comptonscattercompensationstrategiescanbecategorizedintomethodsthatcompensateforscatterdirectlyontheprojectiondata(pre-reconstruction),methodsthatcompensateforscatterduringthereconstructionprocessandpost-reconstructionrestorationfiltering[38].Methodsthatoperateonprojectiondatausuallyaccomplishthecompensationbysubtractingthescatterestimatefrommeasuredprojectiondata.Scatterisoftenestimatedbyscalingprojectiondataacquiredinoneortwoscatterwindowswithpre-determinedfactors[46,47,48].Thescattercanalsobeestimatedbyanalysingthespectrumofprojectiondataacquiredinmultiplenarrow(1–4keVwide)energywindowsusingspectralfittingtechniquesorartificialneuralnetworks[49,50].Post-reconstructionfilteringusuallycompensatesforthescatterbydeconvolvingthereconstructedimageswithascatterresponsefunction(ScRF)acquiredthroughexperimentalmeasurementsorderivedfromMonteCarlosimulations[51].Bothpre-andpost-reconstructionscattercompensationmethods

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makesimpleassumptionsaboutthescattercomponent,suchasshift-invariance,whichresultsinbiasedscatterestimates.Furthermore,scattercompensationisachievedbysubtractionoftheestimatedscattercomponent,whichisusuallyaccompaniedbyalargeincreaseinstatisticalnoise[52,53].

Reconstruction-basedscattercompensation(RBSC)methodscompensateforscatterbymodellingthespatiallyvaryingandobject-dependentSRFintheprojection–back-projectionoperatorswithintheframeworkofaniterativereconstructionalgorithm[37,54].RBSCmethodswereshowntoprovidethemostaccuratescattercompensationamongallmethods[37,38].CurrentlyavailablescattermodellingtechniquesforRBSCincludetheslab-derivedscatterestimation(SDSE)model,theeffectivesourcescatterestimation(ESSE)techniques[54,55],thenon-stochasticnumericalintegrationmethod[56,57],andthefastMonteCarlosimulationapproach[58,59].

Todemonstratetheefficacyofthevariousimagecorrectiontechniques,Fig.8.7showsrepresentativeSPECTbrainimagesreconstructedwithcompensationforvariousdegradingfactors.Theimagequalitygraduallyimproveswhenmoredegradingfactorsarecompensatedfor.Thebestresultisachievedbyincludingcompensationsforallthephysicaldegradingfactors.

Fig.8.7Imagesreconstructedwithcompensationfordifferentphysicaldegradingfactors.Theimagesrepresenttheactualtracerdistributioninthestriatalbrainphantom(a)andreconstructedSPECTimagesobtainedwithoutcompensation(b);withattenuationcompensation(c);withattenuationandcollimator-detectorresponsecompensation(d);andwithattenuation,collimator-detectorresponse,andscattercompensation(e).

Mostofcross-talkcompensationmethodshavebeendevelopedbasedonscattercompensationmethods[60,61,62,63,64,65].Theproceduresincludeestimatingthecross-talk,andsubtractingitpriortoreconstructionorusingreconstruction-basedcompensationapproaches.Thecross-talkcanbeestimatedusingmultipleenergywindowmethods,includingthetriple-energywindowtechniqueormorecomplicatedtechniques,suchasthoseusingartificialneuralnetworks,constrainedspectraldeconvolution,andprincipalcomponentanalysis.Cross-talkmodelswerealsodevelopedforiterativereconstruction-basedcompensationthatuseestimatesoftheactivitydistributionforeachisotopecombinedwiththephysicsoftheimage-formationprocesstoestimatethecross-talk.Forexample,model-basedcross-talkcompensation(MBCC)usestheESSEtechniquetomodelphotoninteractionsinsideobjects,andcollimator-detectorresponsefunctionstomodelphotoninteractionswithinthecollimator-detectorandthefiniteenergyresolutionofthedetector[66].MonteCarlosimulationshavealsobeenusedforcross-talkestimation[58].

Fig.8.8showssimultaneous Tc/ Idual-isotopeimagesreconstructedwithoutcross-talkcompensationandwithMBCC,comparedwithcross-talkfreesingle-isotopeimages.Overall,withoutcompensation,thecross-talkreducedimagecontrastforboth Tcand Iimages.AftercompensationusingMBCC,theimagesareveryclosetosingle-isotopeimages,indicatingthe

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efficacyofcross-talkcompensation[66].

Fig.8.8Simultaneousdual-isotopeimages.Toprow: Tcimages,bottomrow: Iimages.Dual-isotopeimageswithoutcross-talkcompensation(aandd);withMBCC(bande);andcross-talkfreesingle-isotopeimages(candf).

Compensationforcollimator-detectorresponsecanimproveimageresolutionandreducePVEs.However,completecountrecoveryhasnotbeenachievedyet[45,66,67,68].Additionalpartialvolumeeffectcompensationisusuallyrequired.Aplethoraofpartialvolumecompensation(PVC)strategieshavebeenproposed[69,70].SomeofthesemethodsattempttoremoveorreducethePVEsbydeconvolvingtheimagewiththesystempointspreadfunction.Toimprovenoiseproperties,regularizationisrequiredduringdeconvolution.Pixel-by-pixeltemplate-basedapproaches,wherespill-inandspill-outcountsaremodelledbytheireffectontemplateimagesfollowedbycompensationusingsubtractionanddivision,werealsoproposed[71].AlternativeapproachesdirectlycompensateforPVEsattheregionalleveltoprovidecorrectedactivityestimatesusingatransfermatrixofPVEs[72].ApplicationofPVCtodopaminergicneurotransmissionSPECTimaginghasbeenshowntosignificantlyimprovequantitativeaccuracy[73].Reconstruction-basedPVCmethodshavealsobeenproposed,usingforinstance,themaximumaposterioriapproach[74].MethodsthatincorporatePVCdirectlyintothekineticmodellingprocessbyintroducingadditionalparametersthatmodelPVEswerealsoreportedfordynamicimaging[75].Overall,accuratePVCrequiresperfectknowledgeofthesystemresolutionandprecisedelineationofregions-of-interest(ROIs)boundaries,whichcanbeperformedthroughtheuseofregisteredhigh-resolutionanatomicalimagessuchasCTorMRI.

ImagequalityassessmentanddataanalysisQualitativeversusquantitative

Traditionally,SPECTimageshavebeenqualitativelyassessedbyvisualobservationoftraceruptake

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inregionslinkedtoknownpatternsofvariousdiseases.Thevisualassessmentconveysinformationonwhetherthetraceruptakeisnormalorabnormal,and,ifabnormal,itprovidesanideaaboutthemagnitudeoftheabnormality.InbrainSPECT,thevisualassessmentprovidesinformationaboutrighttoleftasymmetriesandwhichstructuresarethemostaffectedbydisease.

However,becausetheuptakeofanimagingtracerisoftenstronglycorrelatedtotheintegrityofphysiologicalfunctionsintargetedROIs,thequantitativemeasurementsoftraceruptakeinthoseROIscanprovidemoreinformationforin-depthassessment.Mostimportantly,quantitativeanalysisenablestoestimatephysiologicalparameters,suchasbloodflow,metabolism,andreceptorconcentration.Thesemeasurementscanbeusedinclinicaldiagnosticwork-uporintheassessmentoftheefficacyoftherapies.Forinstance,thebindingofDATorD2Ragentsinthedopaminergicsystemarestronglycorrelatedtothedegreeofdiseaseprogressioninmovementdisorders.Recentresearchhasdemonstratedthatthequantitativemeasurementoftraceruptakecanprovidemoreinformationforbetterassessmentofmanybraindiseaseprocesses[10,76,77].

Quantificationtechniques

QuantitativestudiesusuallyinvolvedefiningROIsontheimagesandthenmeasuringthetraceuptakeinsidethem.OnecommonlyusedmethodinbrainSPECTistocalculatethespecificbindingpotential(SBP,alsocalledspecificuptakevalue)oftheradiotracerintheROIastheratiobetweenactivityconcentrationinsidetheROIandtheactivityconcentrationsinareferenceregionthathasnospecificbindingforthetracerconsidered.Theimagescanalsobeassessedusingmoresophisticatedvoxel-basedquantitativemethods,suchasstatisticalparametricmapping(SPM),whichautomaticallymapsbrainregionstoastandardizedatlasandcomparestheimageswithadatabaseofnormalsubjects[77].ComparedwithSPM,computingtheSBPgreatlysimplifiesdataanalysisandoftenprovidesareliablemeasurement.

DynamicSPECTcanalsobeusedtoacquireaseriesofimagesoftraceruptakewithintheROIfromtheinjectiontimetillthetimethetraceriswashedoutorthedistributionreachesequilibrium.Consequentialkineticanalysisoftheacquireddynamicdatausingtissuecompartmentmodelsprovidesuniqueinformationthatimprovesthediscriminationbetweenhealthyanddiseasedtissuecomparedwithstaticimages[25].

FutureperspectivesThemajorchallengefacingthefutureofbrainSPECTimagingisthewidespreadadoptionandavailabilityofPETinclinicalsetting.PEThasbetterspatialresolutionandhighersensitivitythanSPECT.TocompetewithPET,futurebrainSPECTsystemsshouldprovideimageswithbetterqualityandimprovedquantitativeaccuracythatisequivalentoratleastcomparablewithPET.

Software

SoftwaredevelopmentinSPECTiscurrentlyfocusingonimprovingimagequalityandquantitativeaccuracyofreconstructedimages.Thisincludesthedevelopmentofnovelreconstructionalgorithmsandimagecorrectiontechniques.Promisingresultshavebeenachievedusingmaximumaposteriori(MAP)reconstructionalgorithmsthatincorporateanatomicalknowledgeintotheSPECTreconstructionprocess.TheanatomicalinformationcanbeobtainedfromMRimages,whichareroutinelyperformedintheclinicforbrainstudies.MRIprovidesdetailedstructural


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informationaboutthebrainwhichismissinginSPECT.InMAPreconstruction,theanatomicalinformationcouldbeusedasapriortoreducestatisticalnoiseandimprovetheresolution,especiallywhenanatomicalregion-basedpriorsareusedtoreducenoiseinsideeachstructurebutnotacrosstheboundaries[74,78,79].Differentpriorscanalsobeusedforeachstructurebasedonitsbiologicalproperties.Fig.8.9showsexamplescomparingOS-EMwithMAPreconstructionsusingdifferentregionalpriors.TheOS-EMimagescontainasignificantamountofnoise.IntheMAPreconstruction,auniformpriorwasusedforthestriatumassuminguniformuptake.Asmoothpriorwasusedforthebackgroundtoreducenoise.ImagesfromMAPreconstructionportraymuchreducednoiseandimprovedspatialresolutionforthestriatumcomparedwithOS-EMreconstruction.

Fig.8.9BrainSPECTimagesreconstructedusingdifferentalgorithmsshowingtheactualtracerdistributioninthestriatalbrainphantom(a).OS-EMreconstruction(b).MAPreconstructionwithonlyauniformpriorforthestriatum(c).MAPreconstructionwithasmoothpriorforbackgroundandauniformpriorforthestriatum(d).

Hardware

AmorefundamentalwayofimprovingSPECTimagesistodevelopnewdetectortechnologythatcouldprovideimageswithhighsensitivityandhighspatialresolution.Detectorsusingsemiconductortechnology,wheredirectphotonconversiondetectorssuchascadmiumtelluridearemostlyused,acquiredataatbetterenergyresolutionthanconventionalgammacameras[80,81].Thiscouldpotentiallyprovideimageswithlessscatterandreducecross-talkindual-isotopeimaging.Developmentinmultimodalityimagingisalsobeingpursued.InSPECT/CT,recentdevelopmentsindual-energyCTscannersmakeitpossibletosegmentsofttissuesinCTimageswithouttheuseofcontrastagents.SimultaneousSPECT/MRIisalsopromisingandshouldenablebothMRandSPECTimagesofpatient’sbraintobeacquiredatthesametime,thusprovidingperfectlyregisteredimages.

AcknowledgementsThisworkwassupportedbytheSwissNationalScienceFoundationundergrantSNSF31003A-149957

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