Physics-Informed Hemodynamic Modeling for Data-Free Prediction and Sparse-Data Assimilation
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 reco...
| Authors | Xi Chen, Jianchuan Yang, Hongde Li et al. |
|---|---|
| Primary category | eess.IV |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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