Learning correspondences of cardiac motion from images using biomechanics-informed modeling. (arXiv:2209.00726v1 [eess.IV])

Learning spatial-temporal correspondences in cardiac motion from images is
important for understanding the underlying dynamics of cardiac anatomical
structures. Many methods explicitly impose smoothness constraints such as the
$\mathcal{L}_2$ norm on the displacement vector field (DVF), while usually
ignoring biomechanical feasibility in the transformation. Other geometric
constraints either regularize specific regions of interest such as imposing
incompressibility on the myocardium or introduce additional steps such as
training a separate network-based regularizer on physically simulated datasets.
In this work, we propose …

arxiv biomechanics images modeling