Study On Reconstruction And Regularization Of Dynamic SPECT

According to the metabolism of body and the marker of isotopic method，body’sphysiological processes can be observed by radioisotope decay. In medicine and biolo-gyfeld, thedevelopmentofDynamicSingle-PhotonEmissionComputedTomography(SPECT) makes the biological processes can be understand better and makes SPECTbecomeimportantindailylife. AlthoughSPECTallowsustomonitoringthebiologicalprocesses, reconstruction of the images from dynamic SPECT data is still a challengeproblem. With the fast decay of radioisotope by time, we can only collect few projec-tions of every image. The projection data contain very little information of the images,especially when noise and tissue deformation present. In this work, we introduce a dy-namic reconstruction model based on sparse representation of unknown images undercertain pre-defned basis. Suppose the voxel of same position of diferent images isa time-activity curve and can be represented by the known curves sparsely. Further-more, the images can also be represented by the curves defned as basis sparsely. Byusing group sparsity of representing coefcients and correlation of geometric struc-tures along temporal direction, we propose a variational model for the reconstructionandregularizationofdynamicSPECTimagesandtheproposedmodelcanbesolvedbythe popular Alternating Direction of Multiplier Method (ADMM). In the reconstruc-tion model, it is somehow the compression of the images for we get the coefcient andhave known the basis. Finally we will show some numerical results on simulated2Dand3D data.