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Proceedings Paper

Reduction of truncation artifacts in CT images via a discriminative dictionary representation method
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Paper Abstract

When the scan field of view (SFOV) of a CT system is not large enough to enclose the entire cross-section of a patient, or the patient needs to be intentionally positioned partially outside the SFOV for certain clinical CT scans, truncation artifacts are often observed in the reconstructed CT images. Conventional wisdom to reduce truncation artifacts is to complete the truncated projection data via data extrapolation with different a priori assumptions. This paper presents a novel truncation artifact reduction method that directly works in the CT image domain. Specifically, a discriminative dictionary that includes a sub-dictionary of truncation artifacts and a sub-dictionary of non-artifact image information was used to separate a truncation artifact-contaminated image into two sub-images, one with reduced truncation artifacts, and the other one containing only the truncation artifacts. Both experimental phantom and retrospective human subject studies have been performed to characterize the performance of the proposed truncation artifact reduction method.

Paper Details

Date Published: 5 April 2016
PDF: 7 pages
Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97831D (5 April 2016); doi: 10.1117/12.2217114
Show Author Affiliations
Yang Chen, Univ. of Wisconsin-Madison (United States)
Southeast Univ. (China)
Ke Li, Univ. of Wisconsin-Madison (United States)
Yinsheng Li, Univ. of Wisconsin-Madison (United States)
Jiang Hsieh, Univ. of Wisconsin-Madison (United States)
GE Healthcare (United States)
Guang-Hong Chen, Univ. of Wisconsin-Madison (United States)

Published in SPIE Proceedings Vol. 9783:
Medical Imaging 2016: Physics of Medical Imaging
Despina Kontos; Thomas G. Flohr, Editor(s)

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