  Late gadolinium enhancement (LGE) cardiac MRI (CMR) is the clinical standard
for diagnosis of myocardial scar. 3D isotropic LGE CMR provides improved
coverage and resolution compared to 2D imaging. However, image acceleration is
required due to long scan times and contrast washout. Physics-guided deep
learning (PG-DL) approaches have recently emerged as an improved accelerated
MRI strategy. Training of PG-DL methods is typically performed in supervised
manner requiring fully-sampled data as reference, which is challenging in 3D
LGE CMR. Recently, a self-supervised learning approach was proposed to enable
training PG-DL techniques without fully-sampled data. In this work, we extend
this self-supervised learning approach to 3D imaging, while tackling challenges
related to small training database sizes of 3D volumes. Results and a reader
study on prospectively accelerated 3D LGE show that the proposed approach at
6-fold acceleration outperforms the clinically utilized compressed sensing
approach at 3-fold acceleration.
