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

Yang Chen; Ke Li; Yinsheng Li; Jiang Hsieh; Guang-Hong Chen

doi:10.1117/12.2217114

5 April 2016 Reduction of truncation artifacts in CT images via a discriminative dictionary representation method

Yang Chen, Ke Li, Yinsheng Li, Jiang Hsieh, Guang-Hong Chen

Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 97831D (2016) https://doi.org/10.1117/12.2217114
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Yang Chen, Ke Li, Yinsheng Li, Jiang Hsieh, and Guang-Hong Chen "Reduction of truncation artifacts in CT images via a discriminative dictionary representation method", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97831D (5 April 2016); https://doi.org/10.1117/12.2217114

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