The purpose of this work is to provide a fast and accurate scatter artifactscorrection algorithm for cone beam CT (CBCT) imaging. The method starts with anestimation of coarse scatter profiles for a set of CBCT data in either imagedomain or projection domain. A denoising algorithm designed specifically forPoisson signals is then applied to derive the final scatter distribution.Qualitative and quantitative evaluations using thorax and abdomen phantoms withMonte Carlo (MC) simulations, experimental Catphan phantom data, and in vivohuman data acquired for a clinical image guided radiation therapy wereperformed. Results show that the proposed algorithm can significantly reducescatter artifacts and recover the correct HU in either projection domain orimage domain. For the MC thorax phantom study, four components segmentationyield the best results, while the results of three components segmentation arestill acceptable. For the Catphan phantom data, the mean value over all pixelsin the residual image is reduced from -21.8 HU to -0.2 HU and 0.7 HU forprojection domain and image domain, respectively. The contrast of the in vivohuman images are greatly improved after correction. The software-basedtechnique has a number of advantages, such as high computational efficiency andaccuracy, and the capability of performing scatter correction without modifyingthe clinical workflow or modifying the imaging hardware. When implementedpractically, this should improve the accuracy of CBCT image quantitation andsignificantly impact CBCT-based interventional procedures and adaptiveradiation therapy.
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