  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.
