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

Abstract

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 thatmore » 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.« less

Authors:

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

• Search DOE PAGES for author "Perdikaris, Paris"
• Search DOE PAGES for ORCID "0000-0002-2816-3229"

---

+ Show Author Affiliations

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

Publication Date:

Tue Sep 17 00:00:00 EDT 2019

Research Org.:

Univ. of Pennsylvania, Philadelphia, PA (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); Physics of Artificial Intelligence program; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Biomedical Imaging and Bio-engineering; National Science Foundation (NSF)

OSTI Identifier:

1595793

Alternate Identifier(s):

OSTI ID: 1562339

Grant/Contract Number:  

SC0019116; HR00111890034; U01-HD087180; T32-EB009384; DGE-1321851

Resource Type:

Accepted Manuscript

Journal Name:

Computer Methods in Applied Mechanics and Engineering

Additional Journal Information:

Journal Volume: 358; Journal Issue: C; Related Information: https://github.com/PredictiveIntelligenceLab/1DBloodFlowPINNs; Journal ID: ISSN 0045-7825

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

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