Synergistic Multi-spectral CT Reconstruction with Directional Total Variation

TL;DR

This paper introduces a multi-spectral CT reconstruction method that fuses energy channel data to produce a shared structural image, enhancing quality and speed through directional total variation regularization.

Contribution

It proposes a novel fusion approach for multi-spectral CT that leverages a polyenergetic image as prior in a directional total variation framework, improving reconstruction quality.

Findings

Enhanced image quality in simulated and experimental data.

Faster reconstruction times compared to existing methods.

Effective use of directional total variation for multi-spectral data.

Abstract

This work considers synergistic multi-spectral CT reconstruction where information from all available energy channels is combined to improve the reconstruction of each individual channel, we propose to fuse this available data (represented by a single sinogram) to obtain a polyenergetic image which keeps structural information shared by the energy channels with increased signal-to-noise-ratio. This new image is used as prior information during a channel-by-channel minimization process through the directional total variation. We analyze the use of directional total variation within variational regularization and iterative regularization. Our numerical results on simulated and experimental data show improvements in terms of image quality and in computational speed.