Database-Free Zero-Shot Deep Learning Reconstruction for Highly-Accelerated Free-Breathing Perfusion CMR

Myocardial perfusion CMR is used to functionally assess coronary artery disease. However, its resolution and coverage remain limited and require rapid imaging. At high accelerations for whole-heart coverage and high spatio-temporal resolution, conventional reconstructions suffer from noise and aliasing artifacts. Physics-guided deep learning (PG-DL) reconstruction has shown improved image quality in fast MRI, but its application to perfusion CMR has been limited due to substantial differences in breathing and contrast uptakes among subjects. In this work, we tackle these challenges by adopting subject-specific self-supervised PG-DL that does not require a training database for simultaneous multi-slice accelerated myocardial perfusion CMR.