Inferring the left atrial appendage (LAA) hemodynamics from 4D CT contrast dynamics: reduced order models (ROMs) and physics informed neural networks (PINNs).

Atrial fibrillation (AF) is a common arrhythmia affecting > 40M people worldwide. During AF, blood inside the LAA becomes stagnant and can form clots, some of which can travel to the brain to cause a stroke. Current tools (CHA2DS2-VASc score) to predict stroke risk in AF patients are not personalized and have modest accuracy. We aim to infer each patient's clotting risk from 4D CT acquisitions of LAA contrast dynamics. We consider ROMs for near-real-time image analysis and high-fidelity PINNs. We run multiple inexpensive ROMs to derive optimal imaging settings balancing predictive accuracy with patient radiation dose. Our ground truth comprises patient-specific CFD simulations, including contrast agent dynamics. We find that advection-diffusion ROMs can infer the average blood residence time in the LAA but fail to capture its fine-scale spatio-temporal features. On the other hand, PINNs, albeit more computationally demanding, can fully infer LAA hemodynamics using each patient's 4D contrast agent concentration fields from CFD as training data. Finally, we show proof of concept of the application of ROMs to infer LAA residence time using 4D CT data acquired in the clinical setting.