Mechanics Informed Fluoroscopy of Esophageal Transport

TL;DR

This paper introduces FluoroMech, a novel method combining neural network segmentation and a 1D physical model to derive quantitative esophageal function metrics from fluoroscopy, aiding diagnosis of esophageal disorders.

Contribution

It presents a new mechanics-informed approach that integrates deep learning and physical modeling for patient-specific esophageal function assessment from fluoroscopy images.

Findings

Accurately predicts flow rate and pressure distribution.

Identifies potential physiomarkers like wall stiffness and muscle relaxation.

Operates with minimal computational time, suitable for clinical use.

Abstract

Fluoroscopy is a radiographic procedure for evaluating esophageal disorders such as achalasia, dysphasia and gastroesophageal reflux disease (GERD). It performs dynamic imaging of the swallowing process and provides anatomical detail and a qualitative idea of how well swallowed fluid is transported through the esophagus. In this work, we present a method called mechanics informed fluoroscopy (FluoroMech) that derives patient-specific quantitative information about esophageal function. FluoroMech uses a Convolutional Neural Network to perform segmentation of image sequences generated from the fluoroscopy, and the segmented images become input to a one-dimensional model that predicts the flow rate and pressure distribution in fluid transported through the esophagus. We have extended this model by developing a FluoroMech reference model to identify and estimate potential physiomarkers such as esophageal wall stiffness and active relaxation ahead of the peristaltic wave in the esophageal musculature. FluoroMech requires minimal computational time, and hence can potentially be applied clinically in the diagnosis of esophageal disorders.