MRI-MECH: Mechanics-informed MRI to estimate esophageal health

TL;DR

MRI-MECH is a novel computational framework that uses physics-informed neural networks to analyze dynamic MRI data, providing detailed estimates of esophageal flow, pressure, and mechanical health, thus aiding diagnosis of esophageal disorders.

Contribution

This work introduces MRI-MECH, a physics-informed neural network approach that models esophageal mechanics from MRI data, including missing information, improving diagnostic capabilities.

Findings

Accurately estimated esophageal fluid velocity and pressure.

Predicted mechanical properties like wall stiffness and relaxation.

Reconstructed missing data in MRI sequences.

Abstract

Dynamic magnetic resonance imaging (MRI) is a popular medical imaging technique to generate 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 then governed by one-dimensional mass and momentum conservation equations. These equations were solved using a physics-informed neural network (PINN). The PINN minimized the difference between the measurements from the MRI and model predictions ensuring 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 enhanced for application to other medical imaging modalities to enhance their functionality as well.