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Found 3 papers, 1 papers with code
Multi-Branch Generative Models for Multichannel Imaging with an Application to PET/CT Joint Reconstruction
This paper presents a proof-of-concept approach for learned synergistic reconstruction of medical images using multi-branch generative models.
Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction
Extensive experiments with simulated and real computed tomography (CT) data were performed to validate the effectiveness of the proposed methods and we reported increased reconstruction accuracy compared to CAOL and iterative methods with single and joint total-variation (TV) regularization.
DUG-RECON: A Framework for Direct Image Reconstruction using Convolutional Generative Networks
The task of medical image reconstruction involves mapping of projection main data collected from the detector to the image domain.
Computed Tomography (CT)
Denoising
+2
Medical Physics
