Research on proton radiography denoising based on deep learning

Cong Sheng; Yu Ding; Ying Tang; Yaping Qi; Man Hu; Jianguang Zhang; Xiangli Cui; Yingying Zhang; Wanli Huo

doi:10.1117/12.3004100

11 October 2023 Research on proton radiography denoising based on deep learning

Cong Sheng, Yu Ding, Ying Tang, Yaping Qi, Man Hu, Jianguang Zhang, Xiangli Cui, Yingying Zhang, Wanli Huo

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 1280034 (2023) https://doi.org/10.1117/12.3004100
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Proton radiography has a broad application prospect in proton radiotherapy. However, it is often affected by scattered protons leading to lower image quality than x-ray images. Traditional image denoising methods can change the measured Water Equivalent Path Length (WEPL), resulting in severe radiation damage to patients. This paper proposes a new proton radiography denoising method. This method focuses on the imaging mechanism and puts forward the corresponding relationship between the distortion of WEPL characteristic curve and energy and share of scattered protons firstly. Then, the powerful feature extraction capabilities of deep learning networks are used to remove the noise to obtain high-precision proton images. Experiments show that this method improves the quality and effectively corrects the WEPL.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Cong Sheng, Yu Ding, Ying Tang, Yaping Qi, Man Hu, Jianguang Zhang, Xiangli Cui, Yingying Zhang, and Wanli Huo "Research on proton radiography denoising based on deep learning", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 1280034 (11 October 2023); https://doi.org/10.1117/12.3004100

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