核医学影像中的数据处理ppt课件

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 Imaging AcquireAcquireProcessProcessApplyApply Scanners Computers Users Outline lData organization lCorrection methods lRebinning lImage reconstruction lImage registration and fusion lDICOM and PACS Outline lData organization lCorrection methods lRebinning lImage reconstruction lImage registration and fusion lDICOM and PACS Data organization lList mode lHistgram lSinogram lLinogram SinogramPET Sinogram r lProjections and Sinogram SinogramPET Sinogram r lProjections and Sinogram SinogramSPECT 2D VS. 3D lSepta between crystal rings lLower sensitivity lLower random lLower scatter l2D reconstruction lNo septa lHigher sensitivity lHigher random lHigher scatter l3D reconstruction or hybrid reconstruction Outline lData organization lCorrection methods lRebinning lImage reconstruction lImage registration and fusion lDICOM and PACS Scatter CoincidenceTrues Coincidence Random Coincidence True Counts 44:291-315. SPECTPET Attenuation correction (con) lMeasured attenuation correction lCoincidence transmission data lLong-lived positron emitter lNormally more than one rod source are used lSinogram windowing is applied provide location of rod lImpractical in 3D lSingles transmission data lShielded point transmission source lSeparate blank scan is needed lSignificant scatter and broad beam Measured attenuation correction lCoincidence measurement using rod source lTransmission measurement using point source CT scan lAdvantage lHigh statistical quality lHigh spatial resolution lSignificant reduction in scan time lDisadvantage lFaster CT, slower PET lSmaller FOV of CT lDifficulty in registration l values do not scale linearly Attenuation correction for PET Types of transmission images Coincident photon Ge- 68/Ga-68 (511 keV) high noise 15-30 min scan time low bias low contrast Single photon Cs-137 (662 keV) lower noise 5-10 min scan time some bias lower contrast X-ray (30-140kVp) no noise 1 min scan time potential for bias high contrast Other attenuation correction methods lCalculated attenuation lRegular geometric outline lConstant tissue lSegmented attenuation lSegment transmission image according to tissue type lAssigning known attenuation coefficients lForward projection attenuation correction Attenuation/Scatter correction University of Pennsylvania PET Center No AC or Scatter CorrAC and Scatter Corr Philips Allegro Arc correction lDifferent sampling distance at different radial position lEqual sampling distance is required in analytical method lInterpolation method lNearest interpolation lLinear interpolation lB-spline interpolation (negative values!) DOIdepth of interaction DOIdepth of interaction（con） Dual Layer A 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 correction lCardiac motion and respiration Motion correction（con） lGated frames lList mode lRespiratory motion is distributed throughout the whole body lImpact is rarely on detection, but often affects quantitation Static wholebodySingle respiratory phase (1 of 7, so noisier) 1 cc lesion on CT Whole-body respiratory gated PET/CT: Patients Partial volume effect lCharacters lObject or structure being imaged only partially occupies the sensitive volume of scanner lSignal amplitude becomes diluted with signals from surrounding structures lThe degree of underestimation of radioactivity concentration will depend not only on its size but also on the relative concentration in surrounding structures lCorrection methods lResolution recovery lUse of anatomical imaging data A 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 effectMAP assumptions: camera moves along circular orbit orbit is reproducible calibration method finds system geometry problem 1: tilting detector assumption: camera moves along circular orbit AORAxial of rotation lOffset of AOR lRotation of AOR lNutation of AOR Camera head tilt lHeads need to be exactly parallel to axis of rotation Correct alignmentHead tilt pinhole calibration Dirk Bequ, Kathleen Vunckx circular orbitcircular orbit + new model extension 2: circular orbit + arbitrary small deviations measurement model Michel Defrise, Chris Vanhove extension 2: circular orbit + arbitrary small deviations old new translationsrotations 1mm -3mm 1.5mm -1.5mm 1.5mm -1mm 1o -2o 1.5o -1.5o 3.5o -2.5o 1mm 1.2 1.4 1.6 1.8 2mm Outline lData organization lCorrection methods lRebinning lImage reconstruction lImage registration and fusion lDICOM and PACS Rebinning lConvert 3D data to 2D SSRB and MSRB lSSRB- Single-slice rebinning lDetection: center slice lSimple lFast lResolution loss lMSRB- Multi-slice rebinning lDistribute along all intermediate slices lDe-blurring along z-axis Fourier rebinning Outline lData organization lCorrection methods lRebinning lImage reconstruction lImage registration and fusion lDICOM and PACS Image reconstruction lAnalytical lFBP lBPF lFDK l3D RP l lIterative lART lMLEM lOSEM lOSLS lMAP l Analytical algorithms lFor example, FBP (Filtered Back- projection) lTreat the unknown image as continuous lPoint-by-point reconstruction lRegular grid points are commonly chosen lTreat projection process as line integral theoretically 解析重建-FBP FBP back projection (BP) = summation of projections filtered back projection (FBP) FDK lFeldkamp、Davis、Kress FDK 3D RPRe-projection Steps of 3D RP lExtract 2D sinograms lReconstruct each with 2D FBP and stack to form 3D image lForward project to calculate missing LORs lExtract 2D projection data of all oblique slices lTake 2D Fourier transform lBack project data through 3D image matrix lRepeat for all angles and oblique slices What is iterative reconstruction lDiscrete measurements, discrete image lOptimization Attractions of iterative methods lEither consistent or inconsistent is OK lComplex geometry lPhysical effects and detection processes can be modeled lNon-negativity lGreat reducing streaking artifacts lBetter contrast recovery l Classification of iteration reconstruction methods lART (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 algorithms lMAP: lMaximize the conditional probability P(image|data) lMLEM: lMaximize the probability P(data|image) Statistical algorithmsGaussian assumption P is projection column matrix, A is system matrix, F image column matrix, C is the covariance matrix of the data Assumed all standard deviations are identical and equal to 1, idealized parallel projection, perfect resolution and no attenuation or other degrading affects Statistical algorithmsPoisson assumption 实测数据 迭代重建-MLEM 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 constant lContents: lDICOM contains additional patients data such as basic information, study information and so on; lGener