  Numerous regularization methods for deformable image registration aim at
enforcing smooth transformations, but are difficult to tune-in a priori and
lack a clear physical basis. Physically inspired strategies have emerged,
offering a sound theoretical basis, but still necessitating complex
discretization and resolution schemes. This study introduces a regularization
strategy that does not require discretization, making it compatible with
current registration frameworks, while retaining the benefits of physically
motivated regularization for medical image registration. The proposed method
performs favorably in both synthetic and real datasets, exhibiting an accuracy
comparable to current state-of-the-art methods.
