WarpPINN: Cine-MR image registration with physics-informed neural networks

Pablo Arratia López, Hernán Mella, Sergio Uribe, Daniel E. Hurtado, Francisco Sahli Costabal

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)


The diagnosis of heart failure usually includes a global functional assessment, such as ejection fraction measured by magnetic resonance imaging. However, these metrics have low discriminate power to distinguish different cardiomyopathies, which may not affect the global function of the heart. Quantifying local deformations in the form of cardiac strain can provide helpful information, but it remains a challenge. In this work, we introduce WarpPINN, a physics-informed neural network to perform image registration to obtain local metrics of heart deformation. We apply this method to cine magnetic resonance images to estimate the motion during the cardiac cycle. We inform our neural network of the near-incompressibility of cardiac tissue by penalizing the Jacobian of the deformation field. The loss function has two components: an intensity-based similarity term between the reference and the warped template images, and a regularizer that represents the hyperelastic behavior of the tissue. The architecture of the neural network allows us to easily compute the strain via automatic differentiation to assess cardiac activity. We use Fourier feature mappings to overcome the spectral bias of neural networks, allowing us to capture discontinuities in the strain field. The algorithm is tested on synthetic examples and on a cine SSFP MRI benchmark of 15 healthy volunteers, where it is trained to learn the deformation mapping of each case. We outperform current methodologies in landmark tracking and provide physiological strain estimations in the radial and circumferential directions. WarpPINN provides precise measurements of local cardiac deformations that can be used for a better diagnosis of heart failure and can be used for general image registration tasks. Source code is available at https://github.com/fsahli/WarpPINN.

Original languageEnglish
Article number102925
Number of pages17
JournalMedical Image Analysis
Early online date9 Aug 2023
Publication statusPublished - 31 Oct 2023

Bibliographical note

Funding Information:
PA is supported by the scholarship from the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics at Bath (SAMBa) , under the project EP/S022945/1 . HM acknowledges the support of ANID - FONDECYT Postdoctorado #3220266 . This work was funded by ANID – Millennium Science Initiative Program – ICN2021_004 to SU and FSC , and NCN19_161 to FSC. FSC also acknowledges the support of the project FONDECYT-Iniciación 11220816 from ANID .


  • Cardiac mechanics
  • Cardiac strain
  • Image registration
  • Physics-informed neural networks

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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