Medical image registration aims at identifying the spatial deformation between images of the same anatomical region and is fundamental to image-based diagnostics and therapy. To date, the majority of the deep learning-based registration methods employ regularizers that enforce global spatial smoothness, e.g., the diffusion regularizer. However, such regularizers are not tailored to the data and might not be capable of reflecting the complex underlying deformation. In contrast, physics-inspired regularizers promote physically plausible deformations. One such regularizer is the linear elastic regularizer which models the deformation of elastic material. These regularizers are driven by parameters that define the material's physical properties. For biological tissue, a wide range of estimations of such parameters can be found in the literature and it remains an open challenge to identify suitable parameter values for successful registration. To overcome this problem and to incorporate physical properties into learning-based registration, we propose to use a hypernetwork that learns the effect of the physical parameters of a physics-inspired regularizer on the resulting spatial deformation field. In particular, we adapt the HyperMorph framework to learn the effect of the two elasticity parameters of the linear elastic regularizer. Our approach enables the efficient discovery of suitable, data-specific physical parameters at test time.

翻译：医学图像配准旨在识别同一解剖区域图像间的空间形变，是基于影像的诊断和治疗的基础。迄今为止，大多数基于深度学习的配准方法采用强制全局空间平滑性的正则化器，例如扩散正则化器。然而，此类正则化器并非针对数据定制，可能无法反映复杂的潜在形变。相比之下，物理启发的正则化器能促进物理上合理的形变。线性弹性正则化器是其中一种，它模拟弹性材料的形变。这些正则化器由定义材料物理性质的参数驱动。对于生物组织，文献中存在多种此类参数的估计值，而确定适合成功配准的参数值仍是一个未解决的挑战。为解决这一问题并将物理性质融入基于学习的配准中，我们提出使用超网络来学习物理启发正则化器的物理参数对最终空间形变场的影响。具体来说，我们改进了HyperMorph框架，以学习线性弹性正则化器中两个弹性参数的影响。我们的方法能够在测试时高效发现适合特定数据的物理参数。