Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning

By Georgios Kissas, Yibo Yang, Eileen Hwuang, Walter R. T. Witschey, John A. Detre, Paris Perdikaris. 2019

483 citations recorded by the source corpus.

Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and surgical planning. Such models can be nowadays deployed on large patient-specific topologies of systemic arterial networks and return detailed predictions on flow patterns, wall shear stresses, and pulse wave propagation. However, their success heavily relies on tedious pre-processing and calibration procedures that typically induce a significant computational cost, thus hampering their clinical applicability. In this work we put forth a machine learning framework that enables the seamless synthesis of non-invasive in-vivo measurement techniques and computational flow dynamics models derived from first physical principles. We illustrate this new paradigm by showing how one-dimensional models of pulsatile flow can be used to constrain the output of deep neural networks such that their predictions satisfy the conservation of mass and momentum principles. Once trained on noisy and scattered clinical data of flow and wall displacement, these networks can return physically consistent predictions for velocity, pressure and wall displacement pulse wave propagation, all without the need to employ conventional simulators. A simple post-processing of these outputs can also provide a cheap and effective way for estimating Windkessel model parameters that are required for the calibration of traditional computational models. The effectiveness of the proposed techniques is demonstrated through a series of prototype benchmarks, as well as a realistic clinical case involving in-vivo measurements near the aorta/carotid bifurcation of a healthy human subject.

Subfields: Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging, Statistical and Nonlinear Physics

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Citations in the corpus

Learning the solution operator of parametric partial differential equations with physics-informed DeepONets (2021, unknown): “Nevertheless, these approaches have found wide applicability across diverse applications including fluid mechanics [19, 20], heat transfer [21], bio-engineering [22, 23], materials [24, 25, 26], and finance [27, 28], showcasing the remarkable effectiveness of ML techniques in learning black-box func”
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks (2020, background): “Despite a series of early promising results [12, 16, 20], the original formulation of Raissi et al. [4] often struggles to handle multi-scale problems.”
Physics-informed deep learning for incompressible laminar flows (2020, methodology): “[10] employed PINN to predict the arterial blood pressure based on the MRI data and the conservation laws.”
Physics-informed neural networks with hard constraints for inverse design (2021, methodology): “Moreover, PINNs have been successfully employed to solve diverse inverse problems , including in optics [8], ﬂuid mechanics [45, 55], systems biology [60], biomedicine [46, 24], and even inverse problems of stochastic PDEs [62] and fractional PDEs [38].”
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next (2022, methodology): “Hyperbolic conservation law is used to simplify the Navier–Stokes equations in hemodynamics (Kissas et al, 2020).”
Multiscale modeling meets machine learning: What can we learn? (2019, methodology): “wall displacements [50], creating computationally efficient surrogates for velocity and pressure fields in intracranial aneurysms”