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Abstract #4270

AI-Driven Quantification of Aortic Diameters from Contrast-Enhanced MRA of the Thoracic Aorta

Charilaos Apostolidis1, Ethan Johnson1, Haben Berhane1, David Dushfunian1, Sebastian Cohn1, Bradley D. Allen1, Aggelos K. Katsaggelos1, and Michael Markl1
1Northwestern University, Chicago, IL, United States

Synopsis

Keywords: Analysis/Processing, Cardiovascular, AI Segmentation, Aortic Disease

Motivation: Aortic disease leads to severe complications. Clinical decision-making relies on contrast-enhanced MR angiography (CEMRA) for manual diameter measurement, which lacks automation and reproducibility. We set to address this by developing an automated measurement method.

Goal(s): To automatically measure aortic diameters with reliable accuracy.

Approach: In CEMRA scans, the thoracic aorta was manually segmented to train and test an AI segmentation model. Segmentations were used to calculate a centerline and position planes perpendicular to the lumen. Diameters were extracted and compared to manually-processed data.

Results: AI-driven analysis was successfully performed in 78% of test set cases and resulted in moderate agreement with ground truth.

Impact: Aortic disease risk assessment relies on imaging-based aortic diameter surveillance. We have developed an automated, AI-driven diameter quantification pipeline, aiming to improve manual processing speed and reproducibility. We have achieved moderate agreement with manual ground truth.

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