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The scatter kernel deconvolution (SKD) method commonly used in clinic requires a Monte Carlo (MC) simulation to determine numerous quality\u2010related kernel parameters, and it cannot realize intelligent scatter kernel parameter optimization, causing limited accuracy of scatter estimation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Our method firstly builds a scatter kernel model to iteratively convolve with raw projections, and then the deep Q\u2010network 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\u2010net based scatter estimation approach for comparison.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The simulation study demonstrates that the mean absolute percentage error (MAPE) of the proposed method is less than 9.72% and the peak signal\u2010to\u2010noise ratio (PSNR) is higher than 23.90\u00a0dB, while for the conventional SKD algorithm, the minimum MAPE is 17.92% and the maximum PSNR is 19.32\u00a0dB. In the measurement study, we adopt a hardware\u2010based beam stop array algorithm to obtain the scatter\u2010free projections as a comparison baseline, and our method can achieve superior performance with MAPE\u00a0&lt;\u00a017.79% and PSNR\u00a0&gt;\u00a016.34\u00a0dB.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16618","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T15:13:36Z","timestamp":1689606816000},"page":"1163-1177","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Adaptive scatter kernel deconvolution modeling for cone\u2010beam CT 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