Physics-Informed Hemodynamic Modeling for Data-Free Prediction and Sparse-Data Assimilation

2026-09-16

AI Summary

为解决冠状动脉介入治疗中现有方法的局限性，提出了一种基于物理信息的血液动力学建模方法，通过深度学习框架从双视图造影中分析3D冠状动脉血流。

Abstract

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.

Problem

• coronary intervention
• angiography
• fractional flow reserve
• hemodynamic modeling
• physical consistency

Innovation

• Physics-informed Hemodynamic Modeling
• Attention-enhanced CNN
• Fourier-encoded Joint Representation
• Embedded Physical Priors
• Sparse-data Assimilation