Sparse constraint-based iterative estimation of effective atomic number and electron density for dual energy CT

Qian Wang; Huiqiao Xie; Tonghe Wang; Justin Roper; Xiangyang Tang; Jeffrey D. Bradley; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2654232

7 April 2023 Sparse constraint-based iterative estimation of effective atomic number and electron density for dual energy CT

Qian Wang, Huiqiao Xie, Tonghe Wang, Justin Roper, Xiangyang Tang, Jeffrey D. Bradley, Tian Liu, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124633G (2023) https://doi.org/10.1117/12.2654232
Event: SPIE Medical Imaging, 2023, 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. For proton therapy treatment planning, besides material-selective images, maps of effective atomic number (Z) and relative electron density to that of water (ρ_e) can also be achieved and further employed to improve stopping power ratio accuracy and reduce range uncertainty. In this work, we propose a one-step iterative estimation method, which employs multi-domain gradient L₀-norm minimization, for Z and ρ_e maps reconstruction. The algorithm was implemented on GPU to accelerate the predictive procedure and to support potential real-time adaptive treatment planning. 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, Tian Liu, and Xiaofeng Yang "Sparse constraint-based iterative estimation of effective atomic number and electron density for dual energy CT", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124633G (7 April 2023); https://doi.org/10.1117/12.2654232

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