{"ID":23475841,"CreatedAt":"2026-09-18T01:09:05.407443952Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.19290","arxiv_id":"2609.19290","title":"Physics-Informed Hemodynamic Modeling for Data-Free Prediction and Sparse-Data Assimilation","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.","short_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 m...","url_abs":"https://arxiv.org/abs/2609.19290","url_pdf":"https://arxiv.org/pdf/2609.19290v1","authors":"[\"Xi Chen\",\"Jianchuan Yang\",\"Hongde Li\",\"Guangxin He\",\"Qiuyu Ye\",\"Qiang Luo\",\"Mao Chen\",\"Wenqi Hu\"]","published":"2026-09-16T18:02:24Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.AI\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}