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

Abstract : 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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Submitted on : Tuesday, October 30, 2007 - 6:06:03 PM
Last modification on : Wednesday, May 29, 2019 - 11:58:03 AM
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Jian Zhou, Jean-Louis Coatrieux, Alexandre Bousse, Huazhong Shu, Limin Luo. A Bayesian MAP-EM Algorithm for PET Image Reconstruction Using Wavelet Transform. IEEE Transactions on Nuclear Science, Institute of Electrical and Electronics Engineers, 2007, 54 (5, Part 1), pp.1660 - 1669. ⟨10.1109/TNS.2007.901200⟩. ⟨inserm-00184255⟩

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