Localizing and quantifying cerebral arterial stenosis from sparse velocity waveforms using a conditional neural operator

Abstract

Cerebral arterial stenosis alters blood flow throughout the vascular network, complicating the estimation of its location and severity from sparse velocity observations. We developed a conditional neural operator that predicts cerebral hemodynamics for candidate stenoses and enables inverse estimation through waveform comparison. Training data were generated using a sequential 0D-1D model that simulates pressure, cross-sectional area, and velocity under varying anatomical, physiological, and stenosis conditions. The operator encodes a single stenosis within vessel tokens and uses global attention to represent its effects across the network. Severity-gated coupling connects these representations to a shared field decoder, while paired healthy simulations constrain baseline predictions. After training, model weights remain fixed, and stenosis location and severity are estimated by minimizing the mismatch between predicted and observed midpoint velocity waveforms. Network-averaged relative validation errors were 0.14% for pressure, 0.59% for area, and 2.79% for velocity. For an eight-vessel observation configuration, localization accuracy across 20 candidate vessels was 99.4% without added noise and 96.0% at the 30% noise setting. Comparisons between configurations with equal numbers of observed vessels showed that measurement placement influenced recovery. Errors and localization ambiguities were greater in selected communicating and distal arteries. These results demonstrate the feasibility of surrogate-based stenosis estimation in simulated cerebral networks and highlight the importance of observation placement. Evaluation was limited to unseen stenosis scenarios within sampled anatomical and physiological conditions. Generalization to new individuals and clinical measurements requires further investigation.