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Accurate myocardial image registration is essential for cardiac strainanalysis and disease diagnosis. However, spectral bias in neural networksimpedes modeling high-frequency deformations, producing inaccurate,biomechanically implausible results, particularly in pathological data. Thispaper addresses spectral bias in physics-informed neural networks (PINNs) byintegrating Fourier Feature mappings and introducing modulation strategies intoa PINN framework. Experiments on two distinct datasets demonstrate that theproposed methods enhance the PINN's ability to capture complex, high-frequencydeformations in cardiomyopathies, achieving superior registration accuracywhile maintaining biomechanical plausibility - thus providing a foundation forscalable cardiac image registration and generalization across multiple patientsand pathologies.

Spectral Bias Correction in PINNs for Myocardial Image Registration of Pathological Data