  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.
