🤖 AI Summary
This study addresses the clinical challenge of directly measuring intravascular pressure by proposing a novel approach that integrates physical principles with deep learning. Specifically, it employs Asymptotic-Preserving Neural Networks (APNN) to simultaneously infer arterial wall viscoelastic parameters and reconstruct the temporal evolution of vascular state variables from Doppler ultrasound-derived cross-sectional area and blood flow velocity data. By embedding asymptotic-preserving properties into the neural network architecture, this method enables end-to-end, physics-consistent learning of parameters in a one-dimensional multiscale viscoelastic blood flow model. Validation on both synthetic and patient-specific datasets demonstrates accurate reconstruction of pressure waveforms, confirming the approach’s effectiveness and robustness in scenarios where direct pressure measurements are unavailable.
📝 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.