Fast and Accurate Inverse Blood Flow Modeling from Minimal Cuff-Pressure Data via PINNs

TL;DR

This paper introduces a fast, noninvasive, patient-specific PINN-based framework for inverse blood flow modeling from minimal cuff-pressure data, enabling accurate hemodynamic assessment with significantly reduced computation time.

Contribution

It develops a novel PINN approach that efficiently solves inverse blood flow problems, outperforming existing models in speed and accuracy, and enables real-time personalized cardiovascular monitoring.

Findings

Model trains in 4000 iterations, at least 10x faster than state-of-the-art.

Achieves high correlation with clinical parameters: r=0.847 for cardiac output and r=0.951 for central systolic blood pressure.

Solves the entire arterial tree with a single network in 5-10 minutes.

Abstract

Accurate assessment of central hemodynamics is essential for diagnosis and risk stratification, yet it still relies largely on invasive measurements or on indirect reconstructions built from population-averaged transfer functions. While conventional methods are valuable in clinical practice, they face limitations, particularly in personalized medicine. Physics-informed methods address these by integrating physical principles, reducing the need for extensive data. In this work, a fully noninvasive, patient-specific framework is developed that combines a validated 1-D model of the systemic arterial tree with physics-informed neural networks (PINNs). This model performs the inverse solution of the flow and pressure fields within the arterial network, given minimal noninvasive measurements of pressure from a cuff reading and trains in 4000 iterations, at least 10x faster than the current state-of-the-art models due to several model enhancements. We validate the model predictions against our 1-D solver, yielding a near perfect correlation, and perform additional tests on a clinical dataset for the identification of important central hemodynamic parameters of cardiac output $CO$ and central systolic blood pressure $cSBP$, with correlations of $r=0.847$ and $r=0.951$, respectively. Moreover, the model is able to tune the patient-specific coefficients of the terminal resistance $R_T$ and compliance $C_T$ while training, treating them as learnable parameters. The inverse PINN model is able to solve the entire tree of 8 arteries with a single network, costing 5-10 minutes of computational time. This significant performance boost compared to traditional iterative inverse methods holds promise towards applications of personalized cardiac output monitoring and hemodynamic assessment via noninvasive approaches like wearable devices.