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Performance improvement and validation of a new MAP reconstruction algorithm

Yu‐Jung TsaiDivision of Medicine, University College London, London, UKAlexandre BousseInstitute of Nuclear Medicine, University College London Hospital, London, UKC.W. StearnsSangtae AhnGE Global Research, Image Reconstruction Lab, Niskayuna, NY, USABrian F. HuttonCentre for Medical Radiation Physics, University of Wollongong, Wollongong, AustraliaSimon ArridgeDepartment of Computer Science, University College London, London, UKKris ThielemansInstitute of Nuclear Medicine, University College London Hospital, London, UK

2016en

ABI

Annotatsiya

We previously proposed a fast maximum a posteriori (MAP) algorithm, limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundary constrains (LBFGS-B-PC), combining LBFGS-B with diagonal preconditioning. Previous results have shown in simulations that it converges using around 40 projections independent of many factors. The aim of this study is to improve the algorithm further by using a better initial image and a modified preconditioner that is less sensitive to noise and data scale. By initializing the algorithm with the best initial image (one full iteration of OSEM with 35 subsets), ROI values can converge almost twice as fast for the same computation time. Moreover, the new preconditioner makes the performance more consistent between high and low count data sets. In addition, we have found a means to choose the stopping criteria to reach a desired level of quantitative accuracy in the reconstructed image. Based on the results with patient data, the optimized LBFGS-B-PC shows promise for clinical imaging.

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Identifikatorlar

DOI: 10.1109/nssmic.2016.8069458

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