Causal discovery of morphological changes in thoracic aorta: Investigation on a large epidemiological non-contrast-enhanced MRI cohort

Motivation: The thoracic aorta is often affected by life-threatening, undetected morphological changes. Prior works primarily focused on factors correlating with aortic aneurysms but lack the investigation of causal influences related to morphological changes. Goal(s): Our goal is to perform automatic aortic shape analysis inline on the scanner. We investigate causal dependencies between metadata and thoracic aortic diameter in approx. 30,000 non-contrast-enhanced MRA. Approach: We apply a deep learning framework for shape analysis and Peter-Clark-algorithm to investigate causal influences on the thoracic aorta. Results: We found that sex, age, height, BMI, hypertension, and vascular-stiffness causally impact the aorta’s diameter, whereas diabetes lacks a causal relationship. Impact: This study reveals causal influences on morphological changes of the thoracic aorta using a large epidemiological dataset (~30,000 non-contrast-enhanced-MRA). A deep-learning-based framework supports the identification of causal factors impacting the aortic diameter and thereby, enabling early detection of life-threatening risks.