Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

Journal Article · Tue Sep 17 00:00:00 EDT 2019 · Computer Methods in Applied Mechanics and Engineering

DOI:https://doi.org/10.1016/j.cma.2019.112623· OSTI ID:1595793

Kissas, Georgios ^[1]; Yang, Yibo ^[2]; Hwuang, Eileen ^[2]; Witschey, Walter R. ^[2]; Detre, John A. ^[2]; Perdikaris, Paris ^[2]

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1. Univ. of Pennsylvania, Philadelphia, PA (United States); University of Pennsylvania
2. Univ. of Pennsylvania, Philadelphia, PA (United States)

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. Here, 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 relatively cheap and effective way for estimating Windkessel model parameters that are required for the calibration of traditional computational models. Finally, 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.

• Research Organization: Univ. of Pennsylvania, Philadelphia, PA (United States)
• Sponsoring Organization: Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Biomedical Imaging and Bio-engineering; National Science Foundation (NSF); Physics of Artificial Intelligence program; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
• Grant/Contract Number: SC0019116
• OSTI ID: 1595793
• Alternate ID(s): OSTI ID: 1562339
• Journal Information: Computer Methods in Applied Mechanics and Engineering, Journal Name: Computer Methods in Applied Mechanics and Engineering Journal Issue: C Vol. 358; ISSN 0045-7825
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

96 KNOWLEDGE MANAGEMENT AND PRESERVATION
97 MATHEMATICS AND COMPUTING
Blood flow modeling
Data-driven modeling
Deep neural networks
Non-invasive diagnostics
Pulse wave propagation