Integrating Machine Learning with Physics-based Modelling of Physiological Systems

Cover image for research topic "Integrating Machine Learning with Physics-based Modelling of Physiological Systems"
10.8K
views
29
authors
6
articles
Editors
2
Impact
Loading...
Original Research
09 June 2023
MRI-MECH: mechanics-informed MRI to estimate esophageal health
Sourav Halder
5 more and 
Neelesh A. Patankar
Measurement losses and residuals along with the total loss. All loss functions were minimized at different rates. The total loss is depicted in red, while the other losses are in blue.

Dynamic magnetic resonance imaging (MRI) is a popular medical imaging technique that generates image sequences of the flow of a contrast material inside tissues and organs. However, its application to imaging bolus movement through the esophagus has only been demonstrated in few feasibility studies and is relatively unexplored. In this work, we present a computational framework called mechanics-informed MRI (MRI-MECH) that enhances that capability, thereby increasing the applicability of dynamic MRI for diagnosing esophageal disorders. Pineapple juice was used as the swallowed contrast material for the dynamic MRI, and the MRI image sequence was used as input to the MRI-MECH. The MRI-MECH modeled the esophagus as a flexible one-dimensional tube, and the elastic tube walls followed a linear tube law. Flow through the esophagus was governed by one-dimensional mass and momentum conservation equations. These equations were solved using a physics-informed neural network. The physics-informed neural network minimized the difference between the measurements from the MRI and model predictions and ensured that the physics of the fluid flow problem was always followed. MRI-MECH calculated the fluid velocity and pressure during esophageal transit and estimated the mechanical health of the esophagus by calculating wall stiffness and active relaxation. Additionally, MRI-MECH predicted missing information about the lower esophageal sphincter during the emptying process, demonstrating its applicability to scenarios with missing data or poor image resolution. In addition to potentially improving clinical decisions based on quantitative estimates of the mechanical health of the esophagus, MRI-MECH can also be adapted for application to other medical imaging modalities to enhance their functionality.

2,568 views
6 citations
Open for submission
Frontiers Logo

Frontiers in Physics

Golden Fractal Jubilee: 50 Years of Bridging Art and Science
Edited by Zbigniew R. Struzik
Deadline
30 September 2025
Submit a paper
Recommended Research Topics
Frontiers Logo

Frontiers in Physiology

Advanced HPC-based Computational Modeling in Biomechanics and Systems Biology
Edited by Mariano Vázquez, Peter V Coveney, Hernan Edgardo Grecco, Alfons Hoekstra, Bastien Chopard
145.8K
views
144
authors
29
articles
Frontiers Logo

Frontiers in Physiology

Clinical Applications of Physiome Models
Edited by Eun Bo Shim, Thomas Heldt, Chae Hun Leem
42.2K
views
83
authors
11
articles
Frontiers Logo

Frontiers in Physiology

Computational Biomechanics for Ventricle-arterial Dysfunction and Remodeling in Heart Failure, Volume I
Edited by Yunlong Huo, Wei Sun, Liang Zhong, Wenchang Tan
41K
views
65
authors
11
articles
Frontiers Logo

Frontiers in Physiology

Mathematical and Computational Methods in Physiology
Edited by Pedro Femia, Juan Melchor, Pedro Carmona-Saez
34.1K
views
25
authors
6
articles
Frontiers Logo

Frontiers in Physiology

Computational Biomechanics for Ventricle-arterial Dysfunction and Remodeling in Heart Failure, Volume II
Edited by Yunlong Huo, Shaun Gregory, Shengzhang Wang
30.2K
views
102
authors
13
articles