Deformable medical image registration is a fundamental task in medical image analysis. While deep learning-based methods have demonstrated superior accuracy and computational efficiency compared to traditional techniques, they often overlook the critical role of regularization in ensuring robustness and anatomical plausibility. We propose DARE (Deformable Adaptive Regularization Estimator), a novel registration framework that dynamically adjusts elastic regularization based on the gradient norm of the deformation field. Our approach integrates strain and shear energy terms, which are adaptively modulated to balance stability and flexibility. To ensure physically realistic transformations, DARE includes a folding-prevention mechanism that penalizes regions with negative deformation Jacobian. This strategy mitigates non-physical artifacts such as folding, avoids over-smoothing, and improves both registration accuracy and anatomical plausibility

翻译：可变形医学图像配准是医学图像分析中的一项基础任务。尽管基于深度学习的方法相较于传统技术在精度和计算效率方面展现出显著优势，但这些方法往往忽视了正则化在确保鲁棒性和解剖合理性方面的关键作用。我们提出DARE（可变形自适应正则化估计器），这是一种新颖的配准框架，能够根据变形场的梯度范数动态调整弹性正则化。该方法整合了应变能和剪切能项，通过自适应调制来平衡稳定性和灵活性。为确保物理上合理的形变，DARE引入了防止折叠机制，对具有负变形雅可比行列式的区域施加惩罚。该策略有效减轻了折叠等非物理伪影，避免了过度平滑，同时提升了配准精度和解剖合理性。