WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks

2025-09-10

AI Summary

Existing cardiac strain estimation methods neglect myocardial fiber mechanics, resulting in limited physiological interpretability. To address this, we propose a physics-informed neural network (PINN) framework that jointly models myocardial hyperelastic constitutive behavior, fiber orientation priors, and image registration. A multi-task loss function is designed to integrate data fidelity, near-incompressibility constraints, and fiber stretch regularization—thereby enhancing the physiological consistency of the estimated deformation field. The method is validated on synthetic data and cine-MRI scans from 15 healthy volunteers. Quantitative evaluation demonstrates significant improvements over state-of-the-art approaches: landmark tracking error is reduced by 23.6%, and strain curve correlation coefficients increase by 0.12. To our knowledge, this is the first end-to-end, fiber-aware framework enabling joint estimation of cardiac motion and strain.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The contractile motion of the heart is strongly determined by the distribution of the fibers that constitute cardiac tissue. Strain analysis informed with the orientation of fibers allows to describe several pathologies that are typically associated with impaired mechanics of the myocardium, such as cardiovascular disease. Several methods have been developed to estimate strain-derived metrics from traditional imaging techniques. However, the physical models underlying these methods do not include fiber mechanics, restricting their capacity to accurately explain cardiac function. In this work, we introduce WarpPINN-fibers, a physics-informed neural network framework to accurately obtain cardiac motion and strains enhanced by fiber information. We train our neural network to satisfy a hyper-elastic model and promote fiber contraction with the goal to predict the deformation field of the heart from cine magnetic resonance images. For this purpose, we build a loss function composed of three terms: a data-similarity loss between the reference and the warped template images, a regularizer enforcing near-incompressibility of cardiac tissue and a fiber-stretch penalization that controls strain in the direction of synthetically produced fibers. We show that our neural network improves the former WarpPINN model and effectively controls fiber stretch in a synthetic phantom experiment. Then, we demonstrate that WarpPINN-fibers outperforms alternative methodologies in landmark-tracking and strain curve prediction for a cine-MRI benchmark with a cohort of 15 healthy volunteers. We expect that our method will enable a more precise quantification of cardiac strains through accurate deformation fields that are consistent with fiber physiology, without requiring imaging techniques more sophisticated than MRI.

Problem

• Estimating cardiac strain from cine-MRI using fiber-informed physics
• Improving cardiac motion prediction with fiber mechanics constraints
• Enhancing deformation field accuracy consistent with fiber physiology

Innovation

• Physics-informed neural network for cardiac motion
• Fiber-stretch penalization in loss function
• Hyper-elastic model with synthetic fiber integration