物理信息血流动力学建模用于无数据预测与稀疏数据同化

中文摘要 AI 辅助

冠状动脉介入治疗的临床决策主要依赖于血管造影和血流储备分数（FFR）。然而，血管造影是二维的，缺乏用于三维病变表征的深度信息，而FFR仅提供单一的功能指标，所给出的血流动力学见解有限。在现有方法中，数值分析计算成本高昂，而基于学习的方法需要大量监督且往往缺乏物理一致性。为解决这些局限，我们提出了物理信息血流动力学建模，这是一种从双视角血管造影进行三维冠状动脉血流分析的综合深度学习框架。首先，注意力增强的CNN从血管造影中重建冠状动脉几何结构。所得点云随后被映射到参考域并进行傅里叶编码以实现联合表示。一个解耦网络分别预测速度场和压力场，嵌入的物理先验使其能够在不同生理条件间高效迁移。在四种血流条件下评估的32例临床患者中，跨狭窄压力降的平均绝对百分比误差为2.02%，速度和压力的相对L2误差分别为0.054和0.023。与医院测量的FFR进行验证进一步达到了93.8%的诊断准确率（30/32；精确95%置信区间，79.2%-99.2%）。该框架还支持说明性血运重建比较和稀疏数据同化，完整的从血管造影到血流动力学的流程在每位患者20分钟内完成。

英文摘要

Clinical decision-making for coronary intervention relies mainly on angiography and fractional flow reserve (FFR). However, angiography is two-dimensional and lacks depth information for 3D lesion characterization, while FFR provides only a single functional index, offering limited hemodynamic insight. Among existing methods, numerical analysis is computationally expensive, whereas learning-based approaches require extensive supervision and often lack physical consistency. To address these limitations, we propose physics-informed hemodynamic modeling, an integrated deep learning framework for 3D coronary blood flow analysis from dual-view angiography. First, an attention-enhanced CNN reconstructs coronary geometry from angiography. The resulting point clouds are then mapped to a reference domain and Fourier-encoded for joint representation. A decoupled network separately predicts velocity and pressure fields, with embedded physical priors enabling efficient transfer across physiological conditions. Across 32 clinical patients evaluated under four flow conditions, the trans-stenotic pressure-drop mean absolute percentage error was 2.02%, while the velocity and pressure relative-L2 errors were 0.054 and 0.023, respectively. Validation against hospital-measured FFR further achieved 93.8% diagnostic accuracy (30/32; exact 95% CI, 79.2%-99.2%). The framework also supports illustrative revascularization comparisons and sparse-data assimilation, with the full angiography-to-hemodynamics pipeline completed within 20 minutes per patient.

发表机构

• The Hong Kong University of Science and Technology（香港科技大学）
• The Hong Kong Polytechnic University（香港理工大学）
• West China Hospital, Sichuan University（四川大学华西医院）

机构由 AI 辅助整理，请以论文原文为准。