Improved cardiac cine MRI using a deep learning-based ESPIRiT reconstruction with video SWIN transformers

Motivation: Cardiac CINE MRI is used clinically for characterizing heart morphology and function, but requires multiple breath-holds to minimize motion artifacts from respiration. Goal(s): Deep learning based ESPIRiT (DL-ESPIRiT) is able to reconstruct dynamic MRI data with high reconstruction accuracy. However, the method still has difficulty resolving fine anatomic structures. We aim to improve reconstruction accuracy using newer transformer architecture. Approach: We replace the ResNet deep learning backbone by a modified Swin Image Restoration network (SwinIR) with video Swin transformers, called DL-ESPIRiT VSwinIRResults: DL-ESPIRiT VSwinIR has substantially improved reconstruction accuracy and can accelerate acquisitions by up to 20x. Impact: Fast CINE acquisitions using our DL-ESPIRiT VSwinIR may enable multiple CINE slices to be acquired in a shortened breath-hold, which will allow heart morphology and function assessment in patients with difficulty breath-holding or following instructions.