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Proceedings Paper

Non-homogeneous updates for the iterative coordinate descent algorithm
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Paper Abstract

Statistical reconstruction methods show great promise for improving resolution, and reducing noise and artifacts in helical X-ray CT. In fact, statistical reconstruction seems to be particularly valuable in maintaining reconstructed image quality when the dosage is low and the noise is therefore high. However, high computational cost and long reconstruction times remain as a barrier to the use of statistical reconstruction in practical applications. Among the various iterative methods that have been studied for statistical reconstruction, iterative coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a novel method for further speeding the convergence of the ICD algorithm, and therefore reducing the overall reconstruction time for statistical reconstruction. The method, which we call nonhomogeneous iterative coordinate descent (NH-ICD) uses spatially non-homogeneous updates to speed convergence by focusing computation where it is most needed. Experimental results with real data indicate that the method speeds reconstruction by roughly a factor of two for typical 3D multi-slice geometries.

Paper Details

Date Published: 7 March 2007
PDF: 12 pages
Proc. SPIE 6498, Computational Imaging V, 64981B (7 March 2007); doi: 10.1117/12.716796
Show Author Affiliations
Zhou Yu, Purdue Univ. (United States)
Jean-Baptiste Thibault, GE Healthcare Technologies (United States)
Charles A. Bouman, Purdue Univ. (United States)
Ken D. Sauer, Univ. of Notre Dame (United States)
Jiang Hsieh, GE Healthcare Technologies (United States)


Published in SPIE Proceedings Vol. 6498:
Computational Imaging V
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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