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A Bayesian MAP-EM Algorithm for PET Image Reconstruction Using Wavelet Transform

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Zhou, Jian | Coatrieux, Jean-Louis | Bousse, Alexandre | Shu, Huazhong | Luo, Limin, M.

Edité par CCSD ; Institute of Electrical and Electronics Engineers -

International audience. In this paper, we present a PET reconstruction method using the wavelet-based maximum a posteriori (MAP) expectation-maximization (EM) algorithm. The proposed method, namely WV-MAP-EM, shows several advantages over conventional methods. It provides an adaptive way for hyperparameter determination. Since the wavelet transform allows the use of fast algorithms, WV-MAP-EM also does not increase the order of computational complexity. The spatial noise behavior (bias/variance and resolution) of the proposed MAP estimator is analyzed. Quantitative comparisons to MAP methods with Markov random field (MRF) prior models point out that our alternative method, wavelet-base method, offers competitive performance in PET image reconstruction.

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