核医学影像中的数据处理.ppt

核医学影像中的数据处理核医学影像中的数据处理Modern Nuclear Medical ImagingAcquireAcquireProcessProcessApplyApplyScanners Computers UsersOutline lData organizationlCorrection methodslRebinning lImage reconstructionlImage registration and fusionlDICOM and PACSOutline lData organizationlCorrection methodslRebinning lImage reconstructionlImage registration and fusionlDICOM and PACSData organizationlList modelHistgramlSinogramlLinogramSinogramPETSinogram rlProjections and SinogramSinogramPETSinogramrlProjections and SinogramSinogramSPECT2D VS.3DlSepta between crystal ringslLower sensitivitylLower randomlLower scatterl2D reconstructionlNo septalHigher sensitivitylHigher randomlHigher scatterl3D reconstruction or hybrid reconstructionOutline lData organizationlCorrection methodslRebinning lImage reconstructionlImage registration and fusionlDICOM and PACSScatter CoincidenceTrues CoincidenceRandom CoincidenceTrue Counts&NoiseNormalizationABCDAttenuationABCDScatterABCDNeed to correct the dataCorrection methodslrandoml“dead time”lnormalizationlscatterlattenuationldecaylArc correctionlDepth of interactionlMotion correctionlPartial volumelAxial of rotationlCamera head tiltRandomFinite time window withEnergy window Coincidence timing windowActivity Random lTail fittinglsimplestlSmall changes in tail,great changes in estimatelEstimation from singles rateslMeasure the single count rate on each detector for a given time windowlSubtracting from the prompts between detector pairlSingles rate is much larger than that of coincidence eventslSingle rates change in the same way over timeDelayed coincidence channel estimationlOne channel is delayed before being sent to coincidence processinglSubtracted form prompt coincidenceslAdvantage lAccuratelSame dead time environment as prompt channellDisadvantage lIncreased system dead timelDoubling of the statistical noise due to randomDead time correctionlDecaying source experiment is performedDead time correction(con)lLook up tablelUniform sourcelKnown quantitylShort livedlLinear extrapolation from count rate for a given level of activityNormalization lCauses of sensitivity variationslSumming of adjacent data elementslDetector efficiency variationslGeometric and solid angle effectslRotational samplinglTime window alignmentlStructural alignmentlseptaSumming of adjacent data elementsGeometric and solid angle effectsRotational samplinglLOR at the edge are sampled less than LOR close to the centerCrystal interface factorsTime window alignment factorsNormalization methods(con)lDirect normalizationlSimplest approachlAdequate statistical qualitylVery uniform activity sourceslScatter in normalization should be substantially different from normal imagingNormalization methods(con)lComponent-based normalizationScatter correctionlLORs recorded outside object boundary can only be explained by scatterlThe scatter distribution is very broadlScattered coincidences fall within the photo-peak window mainly due to scattered once Scatter correctionlEnergy spectra distribution of scattered 511KeV photons according to the number of times each photon scattersScatter correctionlEmpirical scatter correctionslFitting the scatter tailslDirect measurement techniquelEnergy window techniqueslDual energy window methodslMultiple energy window methodslConvolution and de-convolutionlSimulation-based scatter correctionlAnalytical simulationlMonte Carlo simulationFitting the scatter tailslSimplest approachlFit an analytical function to scatter tailslSecond order polynomial or 1D GaussianlCoincidences outside the object are entirely scatter eventslNot always well approximated,particularly in thoraxDirect measurement techniquelOnly applicable to PET with retractable septalStepslMake a measurement of the same object with and without septalScaling septa extended projections for different efficiencylSubtract from projections of polar angle 0lEstimate the oblique scatter by interpolation of the direct plane scatterDual energy window methodsDual energy window methodsMultiple energy window methodsScatter CorrectionAnalytical simulationScatter CorrectionA AB BSingle Scatter-Model based correctionCalculate the contribution for an arbitrary scatter point using the Klein-Nishina equationBeforeScattercorrectionAfterScattercorrectionAttenuation correctionlAttenuation in the body is equal to that of source lying along the same LORZaidi H,Hasegawa B.J Nucl Med 2003;44:291-315.SPECTPETAttenuation correction(con)lMeasured attenuation correctionlCoincidence transmission datalLong-lived positron emitterlNormally more than one rod source are usedlSinogram windowing is applied provide location of rodlImpractical in 3DlSingles transmission datalShielded point transmission sourcelSeparate blank scan is neededlSignificant scatter and broad beamMeasured attenuation correctionlCoincidence measurement using rod sourcelTransmission measurement using point sourceCT scanlAdvantage lHigh statistical qualitylHigh spatial resolutionlSignificant reduction in scan timelDisadvantagelFaster CT,slower PETlSmaller FOV of CTlDifficulty in registrationl values do not scale linearlyAttenuation correction for PETTypes of transmission imagesCoincident photon Ge-68/Ga-68(511 keV)high noise15-30 min scan timelow biaslow contrastSingle photon Cs-137(662 keV)lower noise5-10 min scan timesome biaslower contrastX-ray(30-140kVp)no noise1 min scan timepotential for biashigh contrastOther attenuation correction methodslCalculated attenuationlRegular geometric outlinelConstant tissuelSegmented attenuationlSegment transmission image according to tissue typelAssigning known attenuation coefficientslForward projectionattenuation correctionAttenuation/Scatter correctionUniversity of Pennsylvania PET CenterNo AC or Scatter CorrAC and Scatter CorrPhilips AllegroArc correctionlDifferent sampling distance at different radial positionlEqual sampling distance is required in analytical methodlInterpolation methodlNearest interpolationlLinear interpolationlB-spline interpolation(negative values!)DOIdepth of interactionDOIdepth of interaction（con）Dual LayerA Point Spread Function(PSF)describes the response of an imaging system to a point source or point object.A system that knows the response of a point source from everywhere in its field of view can use this information to recover the original shape and form of imaged objects.PSFs are used in precision imaging instruments,such as microscopy,ophthalmology,and astronomy(e.g.the Hubble telescope)to make geometric corrections to the final image.Point Spread Function(PSF)Motion correctionlCardiac motion and respirationMotion correction（con）lGated frameslList modelRespiratory motion is distributed throughout the whole bodylImpact is rarely on detection,but often affects quantitationStatic wholebodySingle respiratory phase(1 of 7,so noisier)1 cc lesion on CTWhole-body respiratory gated PET/CT:PatientsPartial volume effectlCharacterslObject or structure being imaged only partially occupies the sensitive volume of scannerlSignal amplitude becomes diluted with signals from surrounding structureslThe degree of underestimation of radioactivity concentration will depend not only on its size but also on the relative concentration in surrounding structureslCorrection methodslResolution recoverylUse of anatomical imaging dataA Point Spread Function(PSF)describes the response of an imaging system to a point source or point object.A system that knows the response of a point source from everywhere in its field of view can use this information to recover the original shape and form of imaged objects.PSFs are used in precision imaging instruments,such as microscopy,ophthalmology,and astronomy(e.g.the Hubble telescope)to make geometric corrections to the final image.Point Spread Function(PSF)Partial volume effectMAPassumptions:camera moves along circular orbitorbit is reproducible calibration method finds system geometryproblem 1:tilting detectorassumption:camera moves along circular orbitAORAxial of rotationlOffset of AORlRotation of AORlNutation of AORCamera head tiltlHeads need to be exactly parallel to axis of rotationCorrect alignmentHead tiltpinhole calibrationDirk Bequ,Kathleen Vunckxcircular orbitcircular orbit+new modelextension 2:circular orbit+arbitrary small deviationsmeasurementmodelMichel Defrise,Chris Vanhoveextension 2:circular orbit+arbitrary small deviationsoldnewtranslationsrotations1mm-3mm1.5mm-1.5mm1.5mm-1mm1o-2o1.5o-1.5o3.5o-2.5o1mm1.21.41.61.82mmOutline lData organizationlCorrection methodslRebinning lImage reconstructionlImage registration and fusionlDICOM and PACSRebinninglConvert 3D data to 2DSSRB and MSRBlSSRB-Single-slice rebinninglDetection:center slice lSimple lFast lResolution losslMSRB-Multi-slice rebinninglDistribute along all intermediate sliceslDe-blurring along z-axisFourier rebinningOutline lData organizationlCorrection methodslRebinning lImage reconstructionlImage registration and fusionlDICOM and PACSImage reconstructionlAnalyticallFBPlBPFlFDKl3D RPllIterativelARTlMLEMlOSEMlOSLSlMAPlAnalytical algorithmslFor example,FBP(Filtered Back-projection)lTreat the unknown image as continuouslPoint-by-point reconstructionlRegular grid points are commonly chosenlTreat projection process as line integral theoretically解析重建解析重建-FBPFBPback projection(BP)=summation of projectionsfiltered back projection(FBP)FDKlFeldkamp、Davis、KressFDK3D RPRe-projectionSteps of 3D RPlExtract 2D sinogramslReconstruct each with 2D FBP and stack to form 3D imagelForward project to calculate missing LORslExtract 2D projection data of all oblique sliceslTake 2D Fourier transformlBack project data through 3D image matrixlRepeat for all angles and oblique slicesWhat is iterative reconstructionlDiscrete measurements,discrete imagelOptimizationAttractions of iterative methodslEither consistent or inconsistent is OKlComplex geometrylPhysical effects and detection processes can be modeledlNon-negativity lGreat reducing streaking artifactslBetter contrast recoverylClassification of iteration reconstruction methodslART(algebraic reconstruction techniques)lMART(multiplicative ART)lAART(additive ART)lSIRT(simultaneous iterative reconstruction)lSMART(simultaneously MART)lBI-ART(block iterative ART)lBI-SMART(block iterative SMART)lRBI-SMART(rescaled BI-SMART)Statistical algorithmslMAP:lMaximize the conditional probability P(image|data)lMLEM:lMaximize the probability P(data|image)Statistical algorithmsGaussian assumptionP is projection column matrix,A is system matrix,F image column matrix,C is the covariance matrix of the dataAssumed all standard deviations are identical and equal to 1,idealized parallel projection,perfect resolution and no attenuation or other degrading affectsStatistical algorithmsPoisson assumption实测数据迭代重建迭代重建-MLEM&OSEM正投影比较更新重建MLEM,OSEM,.likelihooditerationSinogramr1 3 2 4Subset order012341040ordered subsets1 iteration of 40 subsets(2 proj per subset)System matrixlScan geometrylCollimator/detector responselAttenuationlScatter(object,collimator,scintillator)lDuty cycle(dwell time at each angle)lDetector efficiencylDead-time losseslPositron rangelNon-colinearitylCrystal penetrationConsiderations of system matrixlQuantitative accuracylSpatial accuracylComputation time lStorage spacelModel uncertaintieslArtifacts due to over simpleificationslSystem matrix trickslFactorizelSymmetrylSparsenesslApproximationlPartial Monte CarlolSystem matrix modelReconstruction image of uniform sourceFBP VS.OSEMlFBPanalyticallPros：lSingle pass lLinearlFastlCons：lStreak artifactlPoor resolutionlCorrection not built-inlOSEMiterationlPros：lBetter resolutionlBetter contrastlLower noiselCons：lExtensive time consuminglMemory consuminglRequired user trainingFBP VS.OSEMlPhantom test(left)lClinical results(right)Outline lData organizationlCorrection methodslRebinning lImage reconstructionlImage registration and fusionlDICOM and PACSImage RegistrationPETCTPET/CTVoxel based image registrationImage RegistrationImage Registrationl算法流程图相似性测量一般用到的函数有相似性测量一般用到的函数有：相同模态图像：残差（sum of square difference）不同模态图像：互信息（mutual information）一般用来做配准的优化算法有：一般用来做配准的优化算法有：六参数或十二参数的优化一般使用 Powell 优化算法多参数优化一般使用LBFGS（limited-memory BroydenFletcherGoldfarbShanno）优化算法（由牛顿算法演变而来）Image FusionlAlpha Blending basedlAdaptive alpha blending Alpha blendingAdaptive Alpha blendingOutline lData organizationlCorrection methodslRebinning lImage reconstructionlImage registration and fusionlDICOM and PACSDICOM and PACSlDICOMlDigital image and Communication in MedicinelCreated by ACR(American College of Radiology)and NEMA(National Electrical Manufacturers Association)in 1985lTwo components:lCommunication protocols and image format standardsDICOM image VS.General imagelStructure:lDICOM contains header and image data sections;lOther image file such as BMP,JPG,TIFF,which contain also two sections;lSize:lThe header size of DICOM is variable;lThe header size of many general image is constantlContents:lDICOM contains additional patients data such as basic information,study information and so on;lGeneral image header describes basic image parameters,such as image size,compression typeDICOM logical layerslPatient information:lPatients name,patients ID,patients birth date,hospital information systemlStudy information:lStudy information such as dose,injection time and additional examination information such as study name study date;lSeries information:lSeries ID,manufacturer and institution namelImage information:lThe size of pixel,image size,pixel value and how it is encodedPACSlPACSlThe Picture Archival and Communication SystemlA system for storage of images and transferring images between computers in different facilities through networkslPACS is helpful to provide comparative studies among different image modalities PACS(con)Thanks Our Group is Our Group is growing up growing up TOF-PETTOF信息信息PET原始数据偶然符合校正发射图像衰变校正死时间校正弓形几何校正衰减校正散射校正归一化校正图像重建数据重组数据校正