19 March 2013 Bregman regularized statistical image reconstruction method and application to prior image constrained compressed sensing (PICCS)
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Proceedings Volume 8668, Medical Imaging 2013: Physics of Medical Imaging; 86683A (2013) https://doi.org/10.1117/12.2008162
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Recently, the Statistical Image Reconstruction (SIR) and compressed sensing (CS) framework has shown promise in the x-ray computed tomography (CT) community. In this paper, we propose to establish an equivalence between the unconstrained optimization problem and a constrained optimization with explicit data consistency term. The immediate consequence of the equivalence is to enable one to use the well-developed optimization method to solve the constrained optimization problem to refine the solution of the corresponding unconstrained optimization problem. As an application of this equivalence, the method was used to develop a convergent and numerically efficient implementation for the prior image constrained compressed sensing (PICCS).
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Yinsheng Li, Yinsheng Li, Pascal Theriault Lauzier, Pascal Theriault Lauzier, Jie Tang, Jie Tang, Guang-Hong Chen, Guang-Hong Chen, } "Bregman regularized statistical image reconstruction method and application to prior image constrained compressed sensing (PICCS)", Proc. SPIE 8668, Medical Imaging 2013: Physics of Medical Imaging, 86683A (19 March 2013); doi: 10.1117/12.2008162; https://doi.org/10.1117/12.2008162
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