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Accurate assessment of left atrial fibrosis in patients with atrialfibrillation relies on high-quality 3D late gadolinium enhancement (LGE) MRIimages. However, obtaining such images is challenging due to patient motion,changing breathing patterns, or sub-optimal choice of pulse sequenceparameters. Automated assessment of LGE-MRI image diagnostic quality isclinically significant as it would enhance diagnostic accuracy, improveefficiency, ensure standardization, and contributes to better patient outcomesby providing reliable and high-quality LGE-MRI scans for fibrosisquantification and treatment planning. To address this, we propose a two-stagedeep-learning approach for automated LGE-MRI image diagnostic qualityassessment. The method includes a left atrium detector to focus on relevantregions and a deep network to evaluate diagnostic quality. We explore twotraining strategies, multi-task learning, and pretraining using contrastivelearning, to overcome limited annotated data in medical imaging. ContrastiveLearning result shows about $4\%$, and $9\%$ improvement in F1-Score andSpecificity compared to Multi-Task learning when there's limited data.

Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images