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

Mohammadreza Movahhedi1, Xin-Yang Liu2, Biao Geng1,3

  • 1Mechanical Engineering Department, University of Maine, Orono, ME, 04469, USA.

Communications Biology
|May 19, 2023
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Summary
This summary is machine-generated.

This study introduces a novel physics-informed neural network to reconstruct 3D tissue dynamics from 2D images. The algorithm accurately captures vocal fold dynamics, aerodynamics, and acoustics, advancing clinical diagnostic capabilities.

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Area of Science:

  • Biomechanics
  • Computational Fluid Dynamics
  • Machine Learning

Background:

  • Tissue dynamics are crucial for physiological functions and clinical diagnosis.
  • Real-time, high-resolution 3D imaging of tissue dynamics presents a significant technical challenge.
  • Current methods often struggle to capture complex flow-induced tissue motion accurately.

Purpose of the Study:

  • To develop and validate a hybrid physics-informed neural network algorithm for inferring 3D flow-induced tissue dynamics from sparse 2D images.
  • To accurately reconstruct physical quantities such as tissue motion, aerodynamics, and acoustics.
  • To overcome the limitations of current imaging techniques for dynamic tissue analysis.

Main Methods:

  • A hybrid algorithm combining a recurrent neural network (RNN) for soft tissue modeling with a differentiable fluid solver.
  • Leveraging solid mechanics principles to project governing equations onto a discrete eigen space.
  • Utilizing a Long Short-Term Memory (LSTM)-based recurrent encoder-decoder architecture connected to a fully connected neural network to model temporal dependencies in flow-structure interactions.

Main Results:

  • The algorithm successfully reconstructed 3D vocal fold dynamics from synthetic data.
  • Accurate inference of aerodynamics and acoustics was achieved from experimental data of pigeon syringes.
  • Demonstrated the algorithm's capability to capture complex temporal dependencies in flow-structure interactions.

Conclusions:

  • The proposed hybrid physics-informed neural network algorithm effectively infers 3D tissue dynamics from sparse 2D data.
  • This approach offers a promising solution for real-time, high-resolution imaging of physiological processes.
  • The method has significant potential for improving clinical diagnosis and understanding of biomechanical systems.