改进神经网络频谱偏差，提升心脏病影像配准精度
Spectral Bias Correction in PINNs for Myocardial Image Registration of Pathological Data
- 用傅里叶特征映射与调制策略缓解PINN的频谱偏差
- 在两个数据集上显著提升高频率形变建模能力
- 适合心血管影像分析与病理数据配准研究者
准确的心肌图像配准对心脏应变分析和疾病诊断至关重要。然而，神经网络中的频谱偏差阻碍了对高频形变的建模，导致结果不准确且生物力学上不可信，尤其在病理数据中更为明显。本文通过将傅里叶特征映射引入物理信息神经网络（PINNs），并设计调制策略，有效缓解了该问题。在两个不同数据集上的实验表明，所提方法显著增强了PINN对心肌病复杂高频形变的捕捉能力，在保持生物力学合理性的同时实现更优的配准精度，为可扩展的心脏影像配准及跨患者、多病种泛化提供了基础。
原文摘要 · Abstract (English)
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.
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