Reconstruction of accelerated MR cholangiopancreatography using supervised and self-supervised 3D Variational Networks

Magnetic resonance cholangiopancreatography suffers from long examination times and artifacts originating from residual motion. To shorten the acquisition protocol, we acquired data at 12-fold undersampling and investigated a 3D Variational Network (VN) architecture for reconstruction. We compared a self-supervised training scheme and a supervised network trained on synthetic data. We find that the self-supervised method is only able to provide competitive reconstructions if the network is initialized with pre-trained weights, and even then does not offer superior performance over the supervised approach. For the presented exemplary data, the supervised VN showed comparable image quality as a reference Compressed Sensing model.