动脉脉搏波在威利斯环中频谱散射的可解释模型编码闭塞位置

中文摘要 AI 辅助

背景与目标。颈动脉多普勒超声是脑血流动力学最广泛可用的床旁探测手段，基于颈动脉速度谱训练的机器学习分类器能够定位颅内闭塞——但代价是黑箱模型、数千个训练样本以及对解剖变异的脆弱性。我们探究是否可以从一个可解释的物理模型中获得相同的信息。方法。我们构建了一个26段威利斯环网络的线性频域一维模型，其中每个心脏谐波在Womersley传输线上传播，并由病变独立散射。该模型针对非线性一维求解器以及两种严重程度下的三维狭窄计算进行了验证，并与基于物理的合成频谱多普勒流程耦合，用于生成包含9,600例病例的虚拟队列，以进行基于物理引导特征的闭塞定位分类。结果。平均血流分配与非线性求解器一致至三位有效数字，谐波幅度在0.90–1.04的比率范围内，颈动脉波形在单次运动学面积校正后相对L2范数误差为6.1%。基准复现了健康血流分配在1–9%以内、侧支通道血流在11–16%以内以及侧支通路层级。物理引导的谐波比率特征从每类五个训练样本中以95.1%的准确率定位十二种病灶类别——比波形驱动的卷积神经网络少一个数量级以上；数据增强部分恢复了对测量噪声和解剖变异的鲁棒性。结论。该框架解释了经验上具有判别性的2–12 Hz多普勒频带的物理起源，并使闭塞定位变得可解释且样本高效。

英文摘要

\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.

发表机构

• State Key Laboratory of Turbulence and Complex Systems, School of Mechanics and Engineering Science, Peking University（湍流与复杂系统国家重点实验室，工程力学系，北京大学）

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