22 March 2010 Non-convex prior image constrained compressed sensing (NC-PICCS)
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The purpose of this paper is to present a new image reconstruction algorithm for dynamic data, termed non-convex prior image constrained compressed sensing (NC-PICCS). It generalizes the prior image constrained compressed sensing (PICCS) algorithm with the use of non-convex priors. Here, we concentrate on perfusion studies using computed tomography examples in simulated phantoms (with and without added noise) and in vivo data, to show how the NC-PICCS method holds potential for dramatic reductions in radiation dose for time-resolved CT imaging. We show that NC-PICCS can provide additional undersampling compared to conventional convex compressed sensing and PICCS, as well as, faster convergence under a quasi-Newton numerical solver.
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Juan Carlos Ramírez Giraldo, Juan Carlos Ramírez Giraldo, Joshua D. Trzasko, Joshua D. Trzasko, Shuai Leng, Shuai Leng, Cynthia H. McCollough, Cynthia H. McCollough, Armando Manduca, Armando Manduca, } "Non-convex prior image constrained compressed sensing (NC-PICCS)", Proc. SPIE 7622, Medical Imaging 2010: Physics of Medical Imaging, 76222C (22 March 2010); doi: 10.1117/12.837239; https://doi.org/10.1117/12.837239

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