Poster + Paper
4 April 2022 Iterative material decomposition with gradient L0-norm minimization for dual-energy CT
Qian Wang, Huiqiao Xie, Tonghe Wang, Justin Roper, Xiangyang Tang, Jeffrey D. Bradley M.D., Tian Liu, Xiaofeng Yang
Author Affiliations +
Conference Poster
Abstract
Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification, artifact suppression, etc. However, the material decomposition of DECT is an ill-posed inverse problem, which is sensitive to noise. To address this issue, we propose an iterative material decomposition method with gradient L0-norm minimization. The proposed method is accelerated by parallel computing technique. The performance of the proposed method is demonstrated via both phantom and patient studies.
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Qian Wang, Huiqiao Xie, Tonghe Wang, Justin Roper, Xiangyang Tang, Jeffrey D. Bradley M.D., Tian Liu, and Xiaofeng Yang "Iterative material decomposition with gradient L0-norm minimization for dual-energy CT", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 1203220 (4 April 2022); https://doi.org/10.1117/12.2611171
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KEYWORDS
Optimization (mathematics)

Signal attenuation

Spatial resolution

X-rays

X-ray computed tomography

X-ray imaging

Cancer

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