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Found 4 papers, 1 papers with code
Semi-Supervised Learning for Dose Prediction in Targeted Radionuclide: A Synthetic Data Study
The aim is to develop an approach that achieves accurate results when PET/CT images are available, but are associated with only a few post-therapy dosimetry data provided by SPECT images.
Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction
This paper presents a novel approach for learned synergistic reconstruction of medical images using multibranch 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
