A novel unpaired learning scheme for accelerated MRI, OT-cycleGAN was extensively applied and was found effective for the reconstruction of multi-coil static MRI.
OT-cycleGAN for the reconstruction of time resolved magnetic resonance angiography (MRA) was proposed. The derived method enables flexible control of sptial and temporal resolution.
Unsupervised deep learning for simultaneous super-resolution and motion artifact removal of diffusion-weighted MRI scans is proposed.
Two-stage unsupervised reconstruction method for 3D TOF-MRA is developed. A novel projection discriminator in the axial reconstruction step drastically enhances the vessel visiblity.
BarbellNet, which consists of long stack of residual channel attention block(RCAB) was proposed for the reconstruction of fast MRI reconstruction. Reconstruction results through this model was placed 6th in the NeurIPS2020 fastMRI challenge.