BackgroundScattering 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.PurposeAiming 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.MethodsOur 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.ResultsThe 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 < 17.79% and PSNR > 16.34 dB.ConclusionsIn 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.
Talbot-Lau grating interferometry (GI) can conduct X-ray phase contrast imaging outside synchrotron facilities and simultaneously provide attenuation, differential phase contrast, and small angle scattering information about imaging samples, where periodically distributed X-ray line sources are required. In this study, we proposed a novel cold-cathode flat-panel X-ray source with micro-array anode target to generate such line sources without using grating G0. Its cathode was composed of densely arranged zinc oxide (ZnO) nanowires, which can generate electrons by field electron emission effect, whereas micro periodic distributed Al-Mo-Al strips were utilized as anode target. X-ray spatial distribution and spectrum of the source with different anode target period (p0) were studied via using EGSnrc. Structured X-ray illumination required for GI was obtained under Mo strips. Mean contrast of the X-ray spatial distribution under the Mo strips of flat panel sources with p0 of 3, 15, 30, 126 m were 33.36%, 81.98%, 91.55%, and 98.63%, respectively. The source can eliminate the use of G0 and thus related limitations of G0 on Talbot-Lau GI.
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