Synergistic Multi-Energy CT Reconstruction with a Deep Penalty “Connecting the Energies”
Résumé
We propose a novel penalty term for multi-channel synergistic image reconstruction with an application to multi-energy computed tomography (CT). The penalty utilizes trained convolutional neural networks (CNNs) to connect the energies to a latent image. We show on simulated data that our method has the potential to outperform reconstruction with a joint total variation (JTV) penalty.
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