Synergistic Multi-spectral CT Reconstruction with Directional Total Variation

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.

arXiv comment17 pages, 17 figures

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• Joint Reconstruction of Multi-channel, Spectral CT Data via Constrained Total Nuclear Variation Minimization
• Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation
• Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
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Cited by in corpus (3)

• Systematic Review on Learning-based Spectral CT
• Uconnect: Synergistic Spectral CT Reconstruction with U-Nets Connecting the Energy bins
• Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction