核医学影像中的数据处理课件

1、核医学影像中的数据处理中国科学院高能物理研究所北京市射线成像技术与装备工程技术研究中心贠明凯Modern Nuclear Medical ImagingAcquireProcessApplyScanners Computers UsersOutline Data organizationCorrection methodsRebinning Image reconstructionImage registration and fusionDICOM and PACSOutline Data organizationCorrection methodsRebinning Image recons

2、tructionImage registration and fusionDICOM and PACSData organizationList modeHistgramSinogramLinogramSinogramPETSinogramrProjections and SinogramSinogramSPECTOutline Data organizationCorrection methodsRebinning Image reconstructionImage registration and fusionDICOM and PACSScatter CoincidenceTrues C

3、oincidenceRandom CoincidenceTrue Counts & NoiseNormalizationABCDAttenuationABCDScatterABCDNeed to correct the dataRandomFinite time window withEnergy window Coincidence timing windowActivity Random Tail fittingsimplestSmall changes in tail, great changes in estimateEstimation from singles ratesMeasu

4、re the single count rate on each detector for a given time windowSubtracting from the prompts between detector pairSingles rate is much larger than that of coincidence eventsSingle rates change in the same way over timeDead time correctionDecaying source experiment is performedDead time correction (

5、con)Look up tableUniform sourceKnown quantityShort livedLinear extrapolation from count rate for a given level of activityNormalization Causes of sensitivity variationsSumming of adjacent data elementsDetector efficiency variationsGeometric and solid angle effectsRotational samplingTime window align

6、mentStructural alignmentseptaSumming of adjacent data elementsGeometric and solid angle effectsRotational samplingLOR at the edge are sampled less than LOR close to the centerCrystal interface factorsTime window alignment factorsNormalization methods (con)Component-based normalizationScatter correct

7、ionLORs recorded outside object boundary can only be explained by scatterThe scatter distribution is very broadScattered coincidences fall within the photo-peak window mainly due to scattered once Scatter correctionEmpirical scatter correctionsFitting the scatter tailsDirect measurement techniqueEne

8、rgy window techniquesDual energy window methodsMultiple energy window methodsConvolution and de-convolutionSimulation-based scatter correctionAnalytical simulationMonte Carlo simulationFitting the scatter tailsSimplest approachFit an analytical function to scatter tailsSecond order polynomial or 1D

9、GaussianCoincidences outside the object are entirely scatter eventsNot always well approximated, particularly in thoraxDirect measurement techniqueOnly applicable to PET with retractable septaStepsMake a measurement of the same object with and without septaScaling septa extended projections for diff

10、erent efficiencySubtract from projections of polar angle 0Estimate the oblique scatter by interpolation of the direct plane scatterDual energy window methodsDual energy window methodsMultiple energy window methodsScatter CorrectionAnalytical simulationScatter CorrectionABSingle Scatter - Model based

11、 correctionCalculate the contribution for an arbitrary scatter point using the Klein-Nishina equationBeforeScattercorrectionAfterScattercorrectionAttenuation correctionAttenuation in the body is equal to that of source lying along the same LORZaidi H, Hasegawa B. J Nucl Med 2003; 44:291-315.SPECTPET

12、Attenuation correction (con)Measured attenuation correctionCoincidence transmission dataLong-lived positron emitterNormally more than one rod source are usedSinogram windowing is applied provide location of rodImpractical in 3DSingles transmission dataShielded point transmission sourceSeparate blank

13、 scan is neededSignificant scatter and broad beamMeasured attenuation correctionCoincidence measurement using rod sourceTransmission measurement using point sourceCT scanAdvantage High statistical qualityHigh spatial resolutionSignificant reduction in scan timeDisadvantageFaster CT, slower PETSmalle

14、r FOV of CTDifficulty in registration 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

15、-140kVp)no noise1 min scan timepotential for biashigh contrastOther attenuation correction methodsCalculated attenuationRegular geometric outlineConstant tissueSegmented attenuationSegment transmission image according to tissue typeAssigning known attenuation coefficientsForward projectionattenuatio

16、n correctionAttenuation/Scatter correctionUniversity of Pennsylvania PET CenterNo AC or Scatter CorrAC and Scatter CorrPhilips AllegroArc correctionDifferent sampling distance at different radial positionEqual sampling distance is required in analytical methodInterpolation methodNearest interpolatio

17、nLinear interpolationB-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

18、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

19、(PSF) Motion correctionCardiac motion and respirationMotion correction（con）Gated framesList modeRespiratory motion is distributed throughout the whole bodyImpact is rarely on detection, but often affects quantitationStatic wholebodySingle respiratory phase(1 of 7, so noisier) 1 cc lesion on CTWhole-

20、body respiratory gated PET/CT: PatientsPartial volume effectCharactersObject or structure being imaged only partially occupies the sensitive volume of scannerSignal amplitude becomes diluted with signals from surrounding structuresThe degree of underestimation of radioactivity concentration will dep

21、end not only on its size but also on the relative concentration in surrounding structuresCorrection methodsResolution recoveryUse 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

22、 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 fi

23、nal 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 rotationOffset of AORRotation of AORNutation of

24、 AORCamera head tiltHeads 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: ci

25、rcular orbit + arbitrary small deviationsoldnewtranslationsrotations1mm-3mm1.5mm-1.5mm1.5mm-1mm1o-2o1.5o-1.5o3.5o-2.5o1mm1.21.41.61.82mmOutline Data organizationCorrection methodsRebinning Image reconstructionImage registration and fusionDICOM and PACSRebinningConvert 3D data to 2DSSRB and MSRBSSRB-

26、 Single-slice rebinningDetection: center slice Simple Fast Resolution lossMSRB- Multi-slice rebinningDistribute along all intermediate slicesDe-blurring along z-axisFourier rebinningOutline Data organizationCorrection methodsRebinning Image reconstructionImage registration and fusionDICOM and PACSIm

27、age reconstructionAnalyticalFBPBPFFDK3D RPIterativeARTMLEMOSEMOSLSMAPAnalytical algorithmsFor example, FBP (Filtered Back-projection)Treat the unknown image as continuousPoint-by-point reconstructionRegular grid points are commonly chosenTreat projection process as line integral theoretically解析重建-FB

28、PFBPback projection (BP) = summation of projectionsfiltered back projection (FBP)FDKFeldkamp、Davis、KressFDK3D RPRe-projectionSteps of 3D RPExtract 2D sinogramsReconstruct each with 2D FBP and stack to form 3D imageForward project to calculate missing LORsExtract 2D projection data of all oblique sli

29、cesTake 2D Fourier transformBack project data through 3D image matrixRepeat for all angles and oblique slicesWhat is iterative reconstructionDiscrete measurements, discrete imageOptimizationAttractions of iterative methodsEither consistent or inconsistent is OKComplex geometryPhysical effects and de

30、tection processes can be modeledNon-negativity Great reducing streaking artifactsBetter contrast recoveryClassification of iteration reconstruction methodsART (algebraic reconstruction techniques)MART (multiplicative ART)AART (additive ART)SIRT (simultaneous iterative reconstruction)SMART (simultane

31、ously MART)BI-ART (block iterative ART)BI-SMART (block iterative SMART)RBI-SMART (rescaled BI-SMART)Statistical algorithmsMAP: Maximize the conditional probability P(image|data)MLEM:Maximize the probability P(data|image)Statistical algorithmsGaussian assumptionP is projection column matrix, A is sys

32、tem 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-MLEM&OSEM正投影比较更新重建MLE

33、M, OSEM, .likelihooditerationSinogramrSubset 1Subset 2Subset 3Subset 41 3 2 4Subset order012341040ordered subsets1 iteration of 40 subsets(2 proj per subset)System matrixScan geometryCollimator/detector responseAttenuationScatter (object, collimator, scintillator)Duty cycle (dwell time at each angle

34、)Detector efficiencyDead-time lossesPositron rangeNon-colinearityCrystal penetrationConsiderations of system matrixQuantitative accuracySpatial accuracyComputation time Storage spaceModel uncertaintiesArtifacts due to over simpleificationsSystem matrix tricksFactorizeSymmetrySparsenessApproximationP

35、artial Monte CarloSystem matrix modelReconstruction image of uniform sourceFBP VS. OSEMFBPanalyticalPros：Single pass LinearFastCons：Streak artifactPoor resolutionCorrection not built-inOSEMiterationPros：Better resolutionBetter contrastLower noiseCons：Extensive time consumingMemory consumingRequired

36、user trainingFBP VS. OSEMPhantom test (left)Clinical results (right)Outline Data organizationCorrection methodsRebinning Image reconstructionImage registration and fusionDICOM and PACSImage RegistrationPETCTPET/CTVoxel based image registrationImage RegistrationImage Registration算法流程图相似性测量一般用到的函数有：相同

37、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：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 FusionAlpha Blending basedAdaptive alpha blending Alpha blendingAdaptive Alpha blendingOutline Data organizationCorrection methodsRebinning Image reconstructionImage registration and fusionDICOM and PACSDICOM and PACSDICOMDigital image and Communication in MedicineCreated by ACR (American College of Radiology) and NEMA (National Electrical Manufacturers Association) in 19