用物理模型+不确定性分析，从普通造影图无创估测心肌缺血风险。
PUNCH: Physics-informed Uncertainty-aware Network for Coronary Hemodynamics
- 结合流体物理模型与变分推断，从造影图像推导血流数据。
- 在20名患者临床数据上准确估算冠状动脉血流储备（CFR）。
- 适合心血管医生用于筛查微血管功能障碍，无需额外检查。
每年全球超过1000万例冠状动脉造影用于诊断阻塞性冠心病，但70%的缺血性心脏病患者未发现明显狭窄。其中约一半存在未被识别的、危及生命的冠状动脉微血管功能障碍（CMD），因缺乏侵入性检测工具而难以发现。本文提出PUNCH，一种基于标准冠状动脉造影的非侵入式、不确定性感知的冠状血流储备（CFR）估计框架。该方法融合物理信息神经网络与变分推断，利用对比剂传输的第一性原理模型，无需真实血流数据或群体训练即可推断血流。每例患者仅需单张GPU运行约三分钟。在含噪声和成像伪影的合成数据及20例临床示踪热稀释数据上验证，PUNCH实现了精准且校准良好的CFR估计。该方法为CMD诊断开辟新范式，展示物理信息推断如何显著提升现有影像的诊断价值。
原文摘要 · Abstract (English)
More than 10 million coronary angiograms are performed globally each year, providing a gold standard for detecting obstructive coronary artery disease. Yet, no obstructive lesions are identified in 70% of patients evaluated for ischemic heart disease. Up to half of these patients have undiagnosed, life-limiting coronary microvascular dysfunction (CMD), which remains under-detected due to the limited availability of invasive tools required to measure coronary flow reserve (CFR). Here, we introduce PUNCH, a non-invasive, uncertainty-aware framework for estimating CFR directly from standard coronary angiography. PUNCH integrates physics-informed neural networks with variational inference to infer coronary blood flow from first-principles models of contrast transport, without requiring ground-truth flow measurements or population-level training. The pipeline runs in approximately three minutes per patient on a single GPU. Validated on synthetic angiograms with controlled noise and imaging artifacts, as well as on clinical bolus thermodilution data from 20 patients, PUNCH demonstrates accurate and uncertainty-calibrated CFR estimation. This approach establishes a new paradigm for CMD diagnosis and illustrates how physics-informed inference can substantially expand the diagnostic utility of available clinical imaging.
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