DL based reconstruction method for an undersampled PROPELLER MRI data

Motivation: Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) is a popular MRI acquisition scheme used for clinical and research MRI data acquisition due to its robustness to motion. However, it is known to have long scan times. Goal(s): Reduce scan time for PROPELLER scans to make it feasible for usage in regular clinical settings. Approach: An unrolled algorithm based deep learning reconstruction method for PROPELLER scans has been proposed, which performs reconstruction at the blade level. Results: Proposed method has been demonstrated to perform good reconstruction on single coil data for multiple anatomies and contrasts. Impact: This method has the potential to reduce PROPELLER scan times and make it a popular choice for acquisition in clinical settings.