Complex Wavelet-Based Sinogram Segmentation for Metal Artifact Reduction in Cone-Beam CT

TL;DR

This paper introduces a novel projection-domain metal artifact reduction method in cone-beam CT using 3D wavelet-based segmentation, improving image quality by effectively reducing streaking artifacts caused by metal objects.

Contribution

It presents an analytical, wavelet-based segmentation approach in the sinogram domain for metal artifact reduction, which is non-learned and improves upon traditional methods.

Findings

Consistently reduces metal artifacts in simulated and clinical data.

Improves visual quality and robustness of CT images.

Outperforms conventional hard-thresholding methods.

Abstract

Metal objects pose a significant challenge in cone-beam computed tomography, as their strong and energy-dependent X-ray attenuation leads to inconsistent projections and severe streaking and shading artifacts in reconstructed images. These artifacts degrade image quality and limit the reliability of subsequent medical analysis. We propose a projection-domain metal artifact reduction method based on analytical metal segmentation in the three-dimensional sinogram using the three-dimensional Dual-Tree Complex Wavelet Transform, where directional wavelet coefficients are exploited to extract the wavefront set and singular support of metal structures. The resulting segmentation enables projection-domain inpainting and artifact-reduced reconstruction by combining metal-free and metal-only reconstructions. The proposed approach is evaluated on both simulated and clinical cone-beam computed tomography data and consistently reduces metal artifacts compared to conventional image-domain hard-thresholding methods. The results demonstrate improved visual quality and robustness in clinically realistic scenarios, highlighting the potential of analytically grounded, non-learned projection-domain segmentation for metal artifact reduction.