Spectral Bias Correction in PINNs for Myocardial Image Registration of Pathological Data
Journal:
arXiv
Published Date:
Apr 24, 2025
Abstract
Accurate myocardial image registration is essential for cardiac strain
analysis and disease diagnosis. However, spectral bias in neural networks
impedes modeling high-frequency deformations, producing inaccurate,
biomechanically implausible results, particularly in pathological data. This
paper addresses spectral bias in physics-informed neural networks (PINNs) by
integrating Fourier Feature mappings and introducing modulation strategies into
a PINN framework. Experiments on two distinct datasets demonstrate that the
proposed methods enhance the PINN's ability to capture complex, high-frequency
deformations in cardiomyopathies, achieving superior registration accuracy
while maintaining biomechanical plausibility - thus providing a foundation for
scalable cardiac image registration and generalization across multiple patients
and pathologies.
