Augmented likelihood image reconstruction with non-local prior image regularization

The presence of high-density objects remains an open problem in medical CT imaging. The recently published Augmented Likelihood Image Reconstruction (ALIR) algorithm has shown to outperform current methods for phantom data and real clinical cases of patients with different kinds of metal implants. A variation of the algorithm with an additional non-local prior image based regularization term is proposed. The prior image should hold anatomical information that are similar to the target image. In every iteration of the ALIR algorithm, a new image is calculated based on the given prior image and a registration step. The resulting image is then used to penalize intensity variations. Reconstruction results show that the regularization step improved the reduction of streaking artifacts.