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Proceedings Paper

Accelerating ordered-subsets X-ray CT image reconstruction using the linearized augmented Lagrangian framework
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Paper Abstract

The augmented Lagrangian (AL) optimization method has drawn more attention recently in imaging applications due to its decomposable structure for composite cost functions and empirical fast convergence rate under weak conditions. However, for problems, e.g., X-ray computed tomography (CT) image reconstruction, where the inner least-squares problem is challenging, the AL method can be slow due to its iterative inner updates. In this paper, using a linearized AL framework, we propose an ordered-subsets (OS) accelerable linearized AL method, OS-LALM, for solving penalized weighted least-squares (PWLS) X-ray CT image reconstruction problems. To further accelerate the proposed algorithm, we also propose a deterministic downward continuation approach for fast convergence without additional parameter tuning. Experimental results show that the proposed algo- rithm significantly accelerates the “convergence” of X-ray CT image reconstruction with negligible overhead and exhibits excellent gradient error tolerance when using many subsets for OS acceleration.

Paper Details

Date Published: 19 March 2014
PDF: 8 pages
Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 903332 (19 March 2014); doi: 10.1117/12.2042686
Show Author Affiliations
Hung Nien, Univ. of Michigan (United States)
Jeffrey A. Fessler, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 9033:
Medical Imaging 2014: Physics of Medical Imaging
Bruce R. Whiting; Christoph Hoeschen, Editor(s)

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