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

doi:10.1117/12.2611171

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

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 1203220 (2022) https://doi.org/10.1117/12.2611171
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

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.

Citation Download Citation

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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