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Purpose:  Due to the increased axial coverage of multislice computed tomography (CT) and the introduction of flat detectors, the size of x‐ray illumination fields has grown dramatically, causing an increase in scatter radiation. For CT imaging, scatter is a significant issue that introduces shading artifact, streaks, as well as reduced contrast and Hounsfield Units (HU) accuracy. The purpose of this work is to provide a fast and accurate scatter artifacts correction algorithm for cone beam CT (CBCT) imaging.    Methods:  The method starts with an estimation of coarse scatter profiles for a set of CBCT data in either image domain or projection domain. A denoising algorithm designed specifically for Poisson signals is then applied to derive the final scatter distribution. Qualitative and quantitative evaluations using thorax and abdomen phantoms with Monte Carlo (MC) simulations, experimental Catphan phantom data, and  in vivo  human data acquired for a clinical image guided radiation therapy were performed. Scatter correction in both projection domain and image domain was conducted and the influences of segmentation method, mismatched attenuation coefficients, and spectrum model as well as parameter selection were also investigated.    Results:  Results show that the proposed algorithm can significantly reduce scatter artifacts and recover the correct HU in either projection domain or image domain. For the MC thorax phantom study, four‐components segmentation yields the best results, while the results of three‐components segmentation are still acceptable. The parameters (iteration number  K  and weight  β ) affect the accuracy of the scatter correction and the results get improved as  K  and  β  increase. It was found that variations in attenuation coefficient accuracies only slightly impact the performance of the proposed processing. For the Catphan phantom data, the mean value over all pixels in the residual image is reduced from −21.8 to −0.2 HU and 0.7 HU for projection domain and image domain, respectively. The contrast of the  in vivo  human images is greatly improved after correction.    Conclusions:  The software‐based technique has a number of advantages, such as high computational efficiency and accuracy, and the capability of performing scatter correction without modifying the clinical workflow (i.e., no extra scan/measurement data are needed) or modifying the imaging hardware. When implemented practically, this should improve the accuracy of CBCT image quantitation and significantly impact CBCT‐based interventional procedures and adaptive radiation therapy.

A model‐based scatter artifacts correction for cone beam CT