Metal Artifact Reduction in CT via Ray Profile Correction

In computed tomography (CT), metal implants increase the inconsistencies between the measured data and the linear assumption made by the analytical 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 the 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. As preliminary work, we built a new MAR framework on fan-beam geometry and tested it to remove simulated metal artifacts on a thorax phantom. The comparison with two representative sinogram correction based MAR methods shows very promising results.