Spectral Bias Correction in PINNs for Myocardial Image Registration of Pathological Data
This work addresses inaccurate cardiac image registration for disease diagnosis, but it is incremental as it builds on existing PINN methods with specific enhancements.
The paper tackled spectral bias in physics-informed neural networks (PINNs) for myocardial image registration, particularly in pathological data, by integrating Fourier Feature mappings and modulation strategies, achieving superior registration accuracy and biomechanical plausibility.
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.