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

arXiv: https://arxiv.org/abs/2211.12549 · HTML: https://mlsanity.com/paper/2211.12549/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 64 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems](https://mlsanity.com/paper/1710.00211/) (2017)
- [Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets](https://mlsanity.com/paper/2103.10974/) (2021)
- [On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks](https://mlsanity.com/paper/2012.10047/) (2020)
- [Analyzing Inverse Problems with Invertible Neural Networks](https://mlsanity.com/paper/1808.04730/) (2018)
- [A unified deep artificial neural network approach to partial differential equations in complex geometries](https://mlsanity.com/paper/1711.06464/) (2017)
- [Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning](https://mlsanity.com/paper/1708.09843/) (2017)
- [Deep learning cardiac motion analysis for human survival prediction](https://mlsanity.com/paper/1810.03382/) (2018)
- [Exploring the potential of transfer learning for metamodels of heterogeneous material deformation](https://mlsanity.com/paper/2010.16260/) (2020)
- [Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach](https://mlsanity.com/paper/1808.08578/) (2018)

## Built on by (most cited first, 14 of 14)

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)
- [Assessment of Left Atrium Motion Deformation Through Full Cardiac Cycle](https://mlsanity.com/paper/2405.17518/) (2024)
- [Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models](https://mlsanity.com/paper/2505.03382/) (2025)
- [FractMorph: A Fractional Fourier-Based Multi-Domain Transformer for Deformable Image Registration](https://mlsanity.com/paper/2508.12445/) (2025)
- [Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy](https://mlsanity.com/paper/2311.14448/) (2023)
- [High-Resolution Maps of Left Atrial Displacements and Strains Estimated with 3D Cine MRI using Online Learning Neural Networks](https://mlsanity.com/paper/2312.09387/) (2023)
- [Enhancing Dynamic CT Image Reconstruction with Neural Fields and Optical Flow](https://mlsanity.com/paper/2406.01299/) (2024)
- [RamPINN: Recovering Raman Spectra From Coherent Anti-Stokes Spectra Using Embedded Physics](https://mlsanity.com/paper/2510.06020/) (2025)
- [Atrial constitutive neural networks](https://mlsanity.com/paper/2504.02748/) (2025)
- [Spectral Bias Correction in PINNs for Myocardial Image Registration of Pathological Data](https://mlsanity.com/paper/2504.17945/) (2025)
- [WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks](https://mlsanity.com/paper/2509.08872/) (2025)
- [Biophysics-Enhanced Neural Representations for Patient-Specific Respiratory Motion Modeling](https://mlsanity.com/paper/2603.22123/) (2026)
- [Learning Cardiac Motion Priors for Implicit Neural Representations](https://mlsanity.com/paper/2607.00955/) (2026)

## Abstract (arXiv)

Heart failure is typically diagnosed with a global function assessment, such as ejection fraction. However, these metrics have low discriminate power, failing to distinguish different types of this disease. 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 the 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 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. We test our algorithm on a synthetic example and on a cine-MRI benchmark of 15 healthy volunteers. We outperform current methodologies both landmark tracking and strain estimation. We expect that WarpPINN will enable more precise diagnostics of heart failure based on local deformation information. Source code is available at https://github.com/fsahli/WarpPINN.