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Related Experiment Video

Updated: Jul 18, 2025

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
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WarpPINN: Cine-MR image registration with physics-informed neural networks.

Pablo Arratia López1, Hernán Mella2, Sergio Uribe3

  • 1Department of Mathematical Sciences, University of Bath, Bath, UK.

Medical Image Analysis
|August 20, 2023
PubMed
Summary

WarpPINN, a novel physics-informed neural network, precisely quantifies local cardiac deformations from MRI scans. This method enhances heart failure diagnosis by providing detailed strain analysis beyond global functional assessments.

Keywords:
Cardiac mechanicsCardiac strainImage registrationPhysics-informed neural networks

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Machine Learning

Background:

  • Current heart failure diagnosis relies on global functional assessments like ejection fraction, which lack specificity for various cardiomyopathies.
  • Quantifying local cardiac deformations (strain) offers valuable diagnostic information but presents significant technical challenges.

Purpose of the Study:

  • Introduce WarpPINN, a physics-informed neural network for accurate cardiac image registration and local deformation measurement.
  • Utilize cine MRI data to estimate cardiac motion and strain during the cardiac cycle.

Main Methods:

  • Developed WarpPINN, a neural network incorporating tissue near-incompressibility via Jacobian penalization.
  • Employed a loss function combining intensity-based similarity and hyperelastic tissue behavior regularization.
  • Integrated Fourier feature mappings to mitigate neural network spectral bias and capture strain field discontinuities.

Main Results:

  • WarpPINN accurately estimates cardiac motion and provides physiological strain measurements in radial and circumferential directions.
  • The method outperforms existing landmark tracking techniques in precision.
  • Demonstrated effectiveness on synthetic data and a benchmark of 15 healthy volunteers' cine SSFP MRI scans.

Conclusions:

  • WarpPINN enables precise local cardiac deformation measurements, improving heart failure diagnosis.
  • The developed physics-informed neural network is applicable to general medical image registration tasks.
  • Accurate strain quantification aids in differentiating cardiomyopathies affecting regional heart function.