Generative Myocardial Motion Tracking via Latent Space Exploration with Biomechanics-informed Prior. (arXiv:2206.03830v1 [eess.IV])

Myocardial motion and deformation are rich descriptors that characterize
cardiac function. Image registration, as the most commonly used technique for
myocardial motion tracking, is an ill-posed inverse problem which often
requires prior assumptions on the solution space. In contrast to most existing
approaches which impose explicit generic regularization such as smoothness, in
this work we propose a novel method that can implicitly learn an
application-specific biomechanics-informed prior and embed it into a neural
network-parameterized transformation model. Particularly, the proposed method …

arxiv biomechanics exploration prior space tracking