Scatter correction with kernel perturbation

Abstract

X-ray scatter degrades image contrast, uniformity and CT number accuracy in cone-beam computed tomography (CBCT). Correction methods based on the scatter kernel superposition (SKS) technique are efficient and suitable for many clinical applications but still produce residual errors due to limitations in the scatter kernel models. To reduce these errors, we propose to generate a first-pass reconstruction using a set of default SKS parameters followed by limited Monte Carlo simulations that are then used to perturb and refine key kernel parameters in order to obtain an improved second-pass correction. To test the approach, we used the fast adaptive scatter kernel model (fASKS) employing asymmetric kernels for the first-pass scatter correction and then used GEANT4 to simulate scatter-to-primary ratios in selected projections allowing for refined scatter estimates. The results show that a minimal number of projections require simulation in order to adequately perturb scatter kernel parameters for all projections. Compared to the default asymmetric kernels, the refined kernels reduced CT number errors from 24 HU to 15 HU in a large pelvis phantom resulting in a more uniform and accurate image.