Mechanics informed fluoroscopy of esophageal transport

Sourav Halder, Shashank Acharya, Wenjun Kou, Peter J. Kahrilas, John E. Pandolfino, Neelesh A. Patankar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Fluoroscopy is a radiographic procedure for evaluating esophageal disorders such as achalasia, dysphasia and gastroesophageal reflux disease. 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 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.

Original languageEnglish (US)
JournalBiomechanics and Modeling in Mechanobiology
DOIs
StateAccepted/In press - 2021

Keywords

  • Convolutional neural network
  • Esophageal active relaxation
  • Esophageal wall stiffness
  • Flexible tube
  • Image segmentation
  • One-dimensional flow

ASJC Scopus subject areas

  • Biotechnology
  • Modeling and Simulation
  • Mechanical Engineering

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