Automatic tuning of CT imaging parameters with reinforcement learning

Haoyu Zhang; Le Shen; Yuxiang Xing

doi:10.1117/12.3006018

1 April 2024 Automatic tuning of CT imaging parameters with reinforcement learning

Haoyu Zhang, Le Shen, Yuxiang Xing

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 1292521 (2024) https://doi.org/10.1117/12.3006018
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Computer tomography (CT) imaging is an essential diagnostic tool in clinical practice. Because of its radioactive nature. It is required to minimize the dose delivered to the patient. Currently, in clinical settings, the adjustment of radiation dose for CT imaging primarily relies on parameters such as tube voltage and tube current, which are often adjusted based on experience, leading to potential unreliability and instability. In this work, we propose a reinforcement learning (RL) based approach for tuning the tube current and voltage according to the principle of As Low As Reasonably Achievable (ALARA). Our method involves the development of an automatic parameter adjustment network (APAN) to determine the optimal policy for parameter adjustment. In this primary study, APAN is trained in a simulation environment and images are reconstructed by the Feldkamp-Davis-Kress (FDK) method, the experiments demonstrate its ability to optimize the parameters to obtain a better dose distribution than a uniform or energy absorption based distribution.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haoyu Zhang, Le Shen, and Yuxiang Xing "Automatic tuning of CT imaging parameters with reinforcement learning", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 1292521 (1 April 2024); https://doi.org/10.1117/12.3006018

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