Comput. Biol. Med. - Three penalized EM-type algorithms for PET image reconstruction.

Tópicos

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Resumo

Based on Bayes theory, Green introduced the maximum a posteriori (MAP) algorithm to obtain a smoothing reconstruction for positron emission tomography. This algorithm is flexible and convenient for most of the penalties, but it is hard to guarantee convergence. For a common goal, Fessler penalized a weighted least squares (WLS) estimator by a quadratic penalty and then solved it with the successive over-relaxation (SOR) algorithm, however, the algorithm was time-consuming and difficultly parallelized. Anderson proposed another WLS estimator for faster convergence, on which there were few regularization methods studied. For three regularized estimators above, we develop three new expectation maximization (EM) type algorithms to solve them. Unlike MAP and SOR, the proposed algorithms yield update rules by minimizing the auxiliary functions constructed on the previous iterations, which ensure the cost functions monotonically decreasing. Experimental results demonstrated the robustness and effectiveness of the proposed algorithms.

Resumo Limpo

base bay theori green introduc maximum posteriori map algorithm obtain smooth reconstruct positron emiss tomographi algorithm flexibl conveni penalti hard guarante converg common goal fessler penal weight least squar wls estim quadrat penalti solv success overrelax sor algorithm howev algorithm timeconsum difficult parallel anderson propos anoth wls estim faster converg regular method studi three regular estim develop three new expect maxim em type algorithm solv unlik map sor propos algorithm yield updat rule minim auxiliari function construct previous iter ensur cost function monoton decreas experiment result demonstr robust effect propos algorithm

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