科研之友
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摘要
PET and SPECT are effective and indispensable imaging tools for the application of medical image reconstruction. Statistical iterative methods for image reconstruction like Maximum Likelihood Expectation Maximization (MLEM) play a significant role in the quality of the images produced by PET/SPECT and allow for accurately modeling the counting statistics and the photon transport during acquisition as reported in literature. The major drawbacks associated with this algorithm include the problem of slow convergence, choice of optimum initial point and ill-posedness. In this paper, an efficient hybrid-cascaded iterative framework for MLEM approach is proposed to alleviate these limitations. This framework consists of breaking the reconstruction process into two parts viz. primary and secondary. During primary part, simultaneous algebraic reconstruction technique (SART) is applied to overcome the problems of slow convergence and initialization. It provides fast convergence and produce good reconstruction results with lesser number of iterations than other iterative methods. The task of primary part is to provide an enhanced image to secondary part to be used as an initial estimate for reconstruction process. The secondary part is a hybrid combination of two parts namely the reconstruction part and the prior part. The reconstruction is done using MLEM algorithm while median anisotropic diffusion (MedAD) filter is used as prior to deal with ill-posedness. The comparative analysis of the proposed method with other standard methods existing in literature is presented for four different test phantoms both qualitatively and quantitatively. Using cascaded primary and secondary reconstruction steps, yields significant improvements in reconstructed image quality. It also accelerates the convergence and provides enhanced results using the projection data. The obtained results justify the applicability of the proposed method.
- 出版日期2016-8
