Physics-inspired regularization is desired for intra-patient image registration since it can effectively capture the biomechanical characteristics of anatomical structures. However, a major challenge lies in the reliance on physical parameters: Parameter estimations vary widely across the literature, and the physical properties themselves are inherently subject-specific. In this work, we introduce a novel data-driven method that leverages hypernetworks to learn the tissue-dependent elasticity parameters of an elastic regularizer. Notably, our approach facilitates the estimation of patient-specific parameters without the need to retrain the network. We evaluate our method on three publicly available 2D and 3D lung CT and cardiac MR datasets. We find that with our proposed subject-specific tissue-dependent regularization, a higher registration quality is achieved across all datasets compared to using a global regularizer. The code is available at https://github.com/compai-lab/2024-miccai-reithmeir.

翻译：在患者内部图像配准中，物理启发的正则化因其能有效捕捉解剖结构的生物力学特性而备受青睐。然而，一个主要挑战在于其对物理参数的依赖：文献中的参数估计差异很大，且物理属性本身具有固有的个体特异性。在本工作中，我们提出了一种新颖的数据驱动方法，利用超网络来学习弹性正则化器中组织依赖的弹性参数。值得注意的是，我们的方法能够在无需重新训练网络的情况下，实现对患者特异性参数的估计。我们在三个公开可用的2D和3D肺部CT及心脏MR数据集上评估了我们的方法。我们发现，与使用全局正则化器相比，采用我们提出的个体特异性组织依赖正则化方法，在所有数据集上均实现了更高的配准质量。代码可在 https://github.com/compai-lab/2024-miccai-reithmeir 获取。