Asymptotic-Preserving Neural Networks for Viscoelastic Parameter Identification in Multiscale Blood Flow Modeling

TL;DR

This paper introduces Asymptotic-Preserving Neural Networks to reliably identify viscoelastic parameters in multiscale blood flow models, enabling pressure waveform estimation from non-invasive ultrasound data.

Contribution

It develops a physics-informed neural network framework that embeds multiscale blood flow physics to infer arterial properties and reconstruct pressure waveforms from ultrasound measurements.

Findings

Effective parameter inference in synthetic and real scenarios.

Accurate pressure waveform reconstruction from ultrasound data.

Framework preserves physical limits in multiscale blood flow modeling.

Abstract

Mathematical models and numerical simulations offer a non-invasive way to explore cardiovascular phenomena, providing access to quantities that cannot be measured directly. In this study, we start with a one-dimensional multiscale blood flow model that describes the viscoelastic properties of arterial walls, and we focus on improving its practical applicability by addressing a major challenge: determining, in a reliable way, the viscoelastic parameters that control how arteries deform under pulsatile pressure. To achieve this, we employ Asymptotic-Preserving Neural Networks that embed the governing physical principles of the multiscale viscoelastic blood flow model within the learning procedure. This framework allows us to infer the viscoelastic parameters while simultaneously reconstructing the time-dependent evolution of the state variables of blood vessels. With this approach, pressure waveforms are estimated from readily accessible patient-specific data, i.e., cross-sectional area and velocity measurements from Doppler ultrasound, in vascular segments where direct pressure measurements are not available. Different numerical simulations, conducted in both synthetic and patient-specific scenarios, show the effectiveness of the proposed methodology.