An interpretable model of spectral scattering of arterial pulse waves in the circle of Willis encodes occlusion location
Abstract
\textit{Background and objective.} Carotid Doppler ultrasound is the most widely available bedside probe of cerebral haemodynamics, and machine-learning classifiers fed carotid velocity spectra can localise intracranial occlusions---but at the price of black-box models, thousands of training samples, and fragility to anatomical variants. We ask whether the same information can be obtained from an interpretable physical model. \textit{Methods.} We formulate a linear frequency-domain one-dimensional model of a 26-segment circle-of-Willis network in which each cardiac harmonic propagates on a Womersley transmission line and is scattered independently by a lesion. The model is validated against a nonlinear one-dimensional solver and three-dimensional stenosis computations at two severities, coupled to a physics-based synthetic spectral-Doppler pipeline, and used to generate a 9{,}600-case virtual cohort for occlusion-localisation classification with physics-guided features. \textit{Results.} Mean flow divisions agree with the nonlinear solver to three significant figures, harmonic magnitudes within a ratio of 0.90--1.04, and carotid waveforms to 6.1\,\% in relative L2 norm after a single kinematic area correction. The benchmark reproduces healthy flow splits within 1--9\,\%, collateral-channel flows within 11--16\,\%, and the collateral-pathway hierarchy. Physics-guided harmonic-ratio features localise twelve lesion classes at 95.1\,\% accuracy from five training samples per class---over an order of magnitude fewer than waveform-driven convoluted neural networks; augmentation partially restores robustness to measurement noise and anatomical variants. \textit{Conclusions.} The framework explains the physical origin of the empirically discriminative 2--12\,Hz Doppler band and makes occlusion localisation interpretable and sample-efficient.
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BibTeX
@misc{huang2026interpretable,
title = {{An interpretable model of spectral scattering of arterial pulse waves in the circle of Willis encodes occlusion location}},
author = {Xun Huang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.20858},
eprint = {2609.20858},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.20858},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - An interpretable model of spectral scattering of arterial pulse waves in the circle of Willis encodes occlusion location
AU - Xun Huang
PY - 2026
DA - 2026/09/04
PB - arXiv
DO - 10.48550/arxiv.2609.20858
UR - https://arxiv.org/abs/2609.20858
AB - \textit{Background and objective.} Carotid Doppler ultrasound is the most widely available bedside probe of cerebral haemodynamics, and machine-learning classifiers fed carotid velocity spectra can localise intracranial occlusions---but at the price of black-box models, thousands of training samples, and fragility to anatomical variants. We ask whether the same information can be obtained from an interpretable physical model. \textit{Methods.} We formulate a linear frequency-domain one-dimensional model of a 26-segment circle-of-Willis network in which each cardiac harmonic propagates on a Womersley transmission line and is scattered independently by a lesion. The model is validated against a nonlinear one-dimensional solver and three-dimensional stenosis computations at two severities, coupled to a physics-based synthetic spectral-Doppler pipeline, and used to generate a 9{,}600-case virtual cohort for occlusion-localisation classification with physics-guided features. \textit{Results.} Mean flow divisions agree with the nonlinear solver to three significant figures, harmonic magnitudes within a ratio of 0.90--1.04, and carotid waveforms to 6.1\,\% in relative L_2 norm after a single kinematic area correction. The benchmark reproduces healthy flow splits within 1--9\,\%, collateral-channel flows within 11--16\,\%, and the collateral-pathway hierarchy. Physics-guided harmonic-ratio features localise twelve lesion classes at 95.1\,\% accuracy from five training samples per class---over an order of magnitude fewer than waveform-driven convoluted neural networks; augmentation partially restores robustness to measurement noise and anatomical variants. \textit{Conclusions.} The framework explains the physical origin of the empirically discriminative 2--12\,Hz Doppler band and makes occlusion localisation interpretable and sample-efficient.
ER -