Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial
  Late Gadolinium Enhanced MRI Images
K M Arefeen Sultan Benjamin Orkild Alan Morris Eugene Kholmovski Erik Bieging Eugene Kwan Ravi Ranjan Ed DiBella Shireen Elhabian
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.