Yu-Jung Tsai, A. Bousse, Matthias Joachim Ehrhardt, B. Hutton, S. Arridge, K. Thielemans
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Performance evaluation of MAP algorithms with different penalties, object geometries and noise levels
A new algorithm (LBFGS-B-PC) which combines ideas of two existing convergent reconstruction algorithms, relaxed separable paraboloidal surrogate (SPS) and limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundary constraints (LBFGS-B), is proposed. Its performance is evaluated in terms of log-posterior value and regional recovery ratio. The results demonstrate the superior convergence speed of the proposed algorithm to relaxed SPS and LBFGS-B, regardless of the noise level, activity distribution, object geometry, and penalties.
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