{
  "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 $L_2$ 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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.20858",
  "author_total": 1,
  "authors": [
    {
      "name": "Xun Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/20mz40sbwe4ay.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.20858",
  "fulltext_url": null,
  "id": "20mz40sbwe4ay",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.20858",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.med-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.20858",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/20mz40sbwe4ay.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:03Z",
      "native_id": "2609.20858",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.20858"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "An interpretable model of spectral scattering of arterial pulse waves in the circle of Willis encodes occlusion location",
  "title_text": "An interpretable model of spectral scattering of arterial pulse waves in the circle of Willis encodes occlusion location",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:03Z",
  "url": "https://research.draconitic.com/paper/20mz40sbwe4ay",
  "venue": "arXiv",
  "versions": []
}