Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks.

Journal: Communications biology
Published Date: May 18, 2023

Abstract

Tissue dynamics play critical roles in many physiological functions and provide important metrics for clinical diagnosis. Capturing real-time high-resolution 3D images of tissue dynamics, however, remains a challenge. This study presents a hybrid physics-informed neural network algorithm that infers 3D flow-induced tissue dynamics and other physical quantities from sparse 2D images. The algorithm combines a recurrent neural network model of soft tissue with a differentiable fluid solver, leveraging prior knowledge in solid mechanics to project the governing equation on a discrete eigen space. The algorithm uses a Long-short-term memory-based recurrent encoder-decoder connected with a fully connected neural network to capture the temporal dependence of flow-structure-interaction. The effectiveness and merit of the proposed algorithm is demonstrated on synthetic data from a canine vocal fold model and experimental data from excised pigeon syringes. The results showed that the algorithm accurately reconstructs 3D vocal dynamics, aerodynamics, and acoustics from sparse 2D vibration profiles.

Authors

  • Mohammadreza Movahhedi
    Mechanical Engineering Department, University of Maine, Orono, ME, 04469, USA.
  • Xin-Yang Liu
    Aerospace and Mechanical Engineering Department, University of Notre Dame, Notre Dame, IN, 46556, USA.
  • Biao Geng
    Mechanical Engineering Department, University of Maine, Orono, ME, 04469, USA.
  • Coen Elemans
    Department of Biology, University of Southern Denmark, Odense M, 5230, Denmark.
  • Qian Xue
    Department of Mechanical Engineering, University of Maine, Room 213, Boardman Hall, Orono, ME 04473.
  • Jian-Xun Wang
    Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana, United States of America.
  • Xudong Zheng
    Department of Mechanical Engineering, University of Maine, Room 213 A, Boardman Hall, Orono, ME 04473.

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