  Accurate assessment of left atrial fibrosis in patients with atrial
fibrillation relies on high-quality 3D late gadolinium enhancement (LGE) MRI
images. However, obtaining such images is challenging due to patient motion,
changing breathing patterns, or sub-optimal choice of pulse sequence
parameters. Automated assessment of LGE-MRI image diagnostic quality is
clinically significant as it would enhance diagnostic accuracy, improve
efficiency, ensure standardization, and contributes to better patient outcomes
by providing reliable and high-quality LGE-MRI scans for fibrosis
quantification and treatment planning. To address this, we propose a two-stage
deep-learning approach for automated LGE-MRI image diagnostic quality
assessment. The method includes a left atrium detector to focus on relevant
regions and a deep network to evaluate diagnostic quality. We explore two
training strategies, multi-task learning, and pretraining using contrastive
learning, to overcome limited annotated data in medical imaging. Contrastive
Learning result shows about $4\%$, and $9\%$ improvement in F1-Score and
Specificity compared to Multi-Task learning when there's limited data.
