Scatter correction for cone-beam computed tomography using self-adaptive scatter kernel superposition

The authors propose a combined scatter reduction and correction method to improve image quality in cone beam computed tomography （CBCT）. The scatter kernel superposition （SKS） method has been used occasionally in previous studies. However, this method differs in that a scatter detecting blocker （SDB） was used between the X-ray source and the tested object to model the self-adaptive scatter kernel. This study first evaluates the scatter kernel parameters using the SDB, and then isolates the scatter distribution based on the SKS. The quality of image can be improved by removing the scatter distribution. The results show that the method can effectively reduce the scatter artifacts, and increase the image quality. Our approach increases the image contrast and reduces the magnitude of cupping. The accuracy of the SKS technique can be significantly improved in our method by using a self-adaptive scatter kernel. This method is computationally effcient, easy to implement, and provides scatter correction using a single scan acquisition.