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Proceedings Paper

Statistical modeling challenges in model-based reconstruction for x-ray CT
Author(s): Ruoqiao Zhang; Aaron Chang; Jean-Baptiste Thibault; Ken Sauer; Charles Bouman
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Paper Abstract

Model- based iterative reconstruction (MBIR) is increasingly widely applied as an improvement over conventional, deterministic methods of image reconstruction in X-ray CT. A primary advantage of MBIR is potentially dras­ tically reduced dosage without diagnostic quality loss. Early success of the method has naturally led to growing numbers of scans at very low dose, presenting data which does not match well the simple statistical models heretofore considered adequate. This paper addresses several issues arising in limiting cases which call for refine­ ment of standard data models. The emergence of electronic noise as a significant contributor to uncertainty, and bias of sinogram values in photon-starved measurements are demonstrated to be important modeling problems in this new environment. We present also possible ameliorations to several of these low-dosage estimation issues.

Paper Details

Date Published: 19 February 2013
PDF: 8 pages
Proc. SPIE 8657, Computational Imaging XI, 86570S (19 February 2013); doi: 10.1117/12.2013231
Show Author Affiliations
Ruoqiao Zhang, Purdue Univ. (United States)
Aaron Chang, Univ. of Notre Dame (United States)
Jean-Baptiste Thibault, GE Healthcare (United States)
Ken Sauer, Univ. of Notre Dame (United States)
Charles Bouman, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 8657:
Computational Imaging XI
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

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