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

作者：Zun Piao, Wenxin Deng, Shuang Huang, Guoqin Lin, Peishan Qin, Xu Li, Wangjiang Wu, Mengke Qi, Linghong Zhou, Bin Li, Jianhui Ma, Yuan Xu · 发表于：Medical Physics · 年份：2023 · DOI：10.1002/mp.16618 · 被引用次数：14 · 研究领域：Medical Imaging Techniques and Applications、Digital Radiography and Breast Imaging、Advanced Radiotherapy Techniques

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 ...