Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography

TL;DR

This paper introduces a novel method for metal artifact reduction in CT by encoding metal mask projection, which preserves geometric information and improves image quality over existing techniques.

Contribution

It proposes a new approach that retains metal-affected regions and encodes metal geometry, leading to more accurate artifact reduction in CT images.

Findings

Outperforms state-of-the-art methods on simulated datasets

Produces more anatomically precise images, especially with large metallic objects

Validated through extensive experiments and expert clinical evaluations

Abstract

Metal artifact reduction (MAR) in computed tomography (CT) is a notoriously challenging task because the artifacts are structured and non-local in the image domain. However, they are inherently local in the sinogram domain. Thus, one possible approach to MAR is to exploit the latter characteristic by learning to reduce artifacts in the sinogram. However, if we directly treat the metal-affected regions in sinogram as missing and replace them with the surrogate data generated by a neural network, the artifact-reduced CT images tend to be over-smoothed and distorted since fine-grained details within the metal-affected regions are completely ignored. In this work, we provide analytical investigation to the issue and propose to address the problem by (1) retaining the metal-affected regions in sinogram and (2) replacing the binarized metal trace with the metal mask projection such that the geometry information of metal implants is encoded. Extensive experiments on simulated datasets and expert evaluations on clinical images demonstrate that our novel network yields anatomically more precise artifact-reduced images than the state-of-the-art approaches, especially when metallic objects are large.