Metal Artifact Reduction in X-ray CT via Ray Profile Correction

Abstract

In computed tomography (CT), metal implants increase the inconsistencies between the measured data and the linear assumption of the Radon transform made by the analytic CT reconstruction algorithm. The inconsistencies appear in the form of dark and bright bands and streaks in the reconstructed image, collectively called metal artifacts. The standard method for metal artifact reduction (MAR) replaces the inconsistent data with interpolated data. However, sinogram interpolation not only introduces new artifacts but it also suffers from the loss of detail near the implanted metals. With the help of a prior image that is usually estimated from the metal artifact-degraded image via computer vision techniques, improvements are feasible but still no MAR method exists that is widely accepted and utilized. We propose a technique that utilizes a prior image from a CT scan taken of the patient before implanting the metal objects. Hence, there is a sufficient amount of structural similarity to cover the loss of detail around the metal implants. Using the prior scan and a segmentation or model of the metal implant, our method then replaces sinogram interpolation with ray profile matching and estimation, which yields much more reliable data estimates for the affected sinogram regions. Experiments with clinical dataset obtained using surgical imaging CT scanner show very promising results.