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Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning

Yu‐Jung TsaiInstitute of Nuclear Medicine, University College London, London, U.KAlexandre BousseInstitute of Nuclear Medicine, University College London, London, U.KMatthias J. EhrhardtDepartment for Applied Mathematics and Theoretical Physics, University ofCambridge, Cambridge, U.KC.W. StearnsSangtae AhnGE Global Research, Niskayuna, NY, USABrian F. HuttonCentre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, AustraliaSimon ArridgeDepartment of Computer Science, University College London, London, U.KKris ThielemansInstitute of Nuclear Medicine, University College London, London, U.K

2017en

ABI

Annotatsiya

This paper reports on the feasibility of using a quasi-Newton optimization algorithm, limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundary constraints (L-BFGS-B), for penalized image reconstruction problems in emission tomography (ET). For further acceleration, an additional preconditioning technique based on a diagonal approximation of the Hessian was introduced. The convergence rate of L-BFGS-B and the proposed preconditioned algorithm (L-BFGS-B-PC) was evaluated with simulated data with various factors, such as the noise level, penalty type, penalty strength and background level. Data of three <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> F-FDG patient acquisitions were also reconstructed. Results showed that the proposed L-BFGS-B-PC outperforms L-BFGS-B in convergence rate for all simulated conditions and the patient data. Based on these results, L-BFGS-B-PC shows promise for clinical application.

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DOI: 10.1109/tmi.2017.2786865

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