A Deep Information Sharing Network for Multi-Contrast Compressed Sensing MRI Reconstruction
A Deep Information Sharing Network for Multi-Contrast Compressed Sensing MRI Reconstruction
Compressed sensing (CS) theory can accelerate multi-contrast magnetic resonance imaging (MRI) by sampling fewer measurements within each contrast. However, conventional optimization-based reconstruction models suffer several limitations, including a strict assumption of shared sparse support, time-consuming optimization, and "shallow" models with difficulties in encoding the patterns contained in massive MRI data. …