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Proceedings Paper

Adaptive smoothing algorithms for MBIR in CT applications
Author(s): Jingyan Xu; Frederic Noo
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Paper Abstract

Many model based image reconstruction (MBIR) methods for x-ray CT are formulated as convex minimization problems. If the objective function is nonsmooth. primal-dual algorithms are applicable with the drawback that there is an increased memory cost due to the dual variables. Some algorithms recently developed for large-scale nonsmooth convex programs use adaptive smoothing techniques and are of the primal type. That is, they achieve convergence without introducing the dual variables, hence without the increased memory. We discuss one such algorithm with an O(1/k) convergence rate, where k is the iteration number. We then present an extension of it to handle strong convex objective functions. This new algorithm has the optimal convergence rate of O(1/k 2) for its problem class. Our preliminary numerical studies demonstrate competitive performance with respect to an alternative method.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110720C (28 May 2019); doi: 10.1117/12.2534928
Show Author Affiliations
Jingyan Xu, Johns Hopkins Univ. (United States)
Frederic Noo, The Univ. of Utah (United States)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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