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Proceedings Paper

Non-convex prior image constrained compressed sensing (NC-PICCS)
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Paper Abstract

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.

Paper Details

Date Published: 22 March 2010
PDF: 8 pages
Proc. SPIE 7622, Medical Imaging 2010: Physics of Medical Imaging, 76222C (22 March 2010); doi: 10.1117/12.837239
Show Author Affiliations
Juan Carlos Ramírez Giraldo, Mayo Clinic (United States)
Joshua D. Trzasko, Mayo Clinic (United States)
Shuai Leng, Mayo Clinic (United States)
Cynthia H. McCollough, Mayo Clinic (United States)
Armando Manduca, Mayo Clinic (United States)


Published in SPIE Proceedings Vol. 7622:
Medical Imaging 2010: Physics of Medical Imaging
Ehsan Samei; Norbert J. Pelc, Editor(s)

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