Adaptive scatter kernel deconvolution modeling for cone‐beam CT scatter correction via deep reinforcement learning

• Published in: Medical physics (Lancaster) 2024-02, Vol.51 (2), p.1163-1177
• Main Authors: Piao, Zun, Deng, Wenxin, Huang, Shuang, Lin, Guoqin, Qin, Peishan, Li, Xu, Wu, Wangjiang, Qi, Mengke, Zhou, Linghong, Li, Bin, Ma, Jianhui, Xu, Yuan
• Format: Article
• Language: English
• Subjects: adaptive scatter kernel optimization; Algorithms; Cone-Beam Computed Tomography - methods; cone‐beam CT scatter correction; deep reinforcement learning; Image Processing, Computer-Assisted - methods; Phantoms, Imaging; scatter kernel deconvolution algorithm; Scattering, Radiation
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.16618

Background Scattering photons can seriously contaminate cone‐beam CT (CBCT) image quality with severe artifacts and substantial degradation of CT value accuracy, which is a major concern limiting the widespread application of CBCT in the medical field. The scatter kernel deconvolution (SKD) method commonly used in clinic requires a Monte Carlo (MC) simulation to determine numerous quality‐related kernel parameters, and it cannot realize intelligent scatter kernel parameter optimization, causing limited accuracy of scatter estimation. Purpose Aiming at improving the scatter estimation accuracy of the SKD algorithm, an intelligent scatter correction framework integrating the SKD with deep reinforcement learning (DRL) scheme is proposed. Methods Our method firstly builds a scatter kernel model to iteratively convolve with raw projections, and then the deep Q‐network of the DRL scheme is introduced to intelligently interact with the scatter kernel to achieve a projection adaptive parameter optimization. The potential of the proposed framework is demonstrated on CBCT head and pelvis simulation data and experimental CBCT measurement data. Furthermore, we have implemented the U‐net based scatter estimation approach for comparison. Results The simulation study demonstrates that the mean absolute percentage error (MAPE) of the proposed method is less than 9.72% and the peak signal‐to‐noise ratio (PSNR) is higher than 23.90 dB, while for the conventional SKD algorithm, the minimum MAPE is 17.92% and the maximum PSNR is 19.32 dB. In the measurement study, we adopt a hardware‐based beam stop array algorithm to obtain the scatter‐free projections as a comparison baseline, and our method can achieve superior performance with MAPE  16.34 dB. Conclusions In this paper, we propose an intelligent scatter correction framework that integrates the physical scatter kernel model with DRL algorithm, which has the potential to improve the accuracy of the clinical scatter correction method to obtain better CBCT imaging quality.