Localizing and quantifying cerebral arterial stenosis from sparse velocity waveforms using a conditional neural operator
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arXiv: 2610.23774v1 · Summary page
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Tracing an arterial narrowing from distant flow signals
A narrowing in a brain artery can change blood flow far beyond its immediate location. Researchers have trained a neural-network surrogate of a cerebral circulation model to exploit those network-wide effects. Given a candidate narrowing, it predicts blood-flow waveforms; comparing those predictions with sparse observations then allows the system to estimate the narrowing’s location and severity.
In simulated tests using observations from eight vessels, the method identified the correct location among 20 candidate vessels in 99.4% of cases without added noise and 96.0% at the study’s 30% noise setting. Measurement placement mattered, and some communicating and distal arteries were harder to distinguish. These are promising simulation results, not clinical diagnostic performance: the preprint explicitly leaves open whether the approach generalizes to new individuals and real patient measurements.
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