Deformable models with sparsity constraints for cardiac motion analysis
We propose a new class of deformable models using sparse regularization. It is able to handle gross errors or outliers robustly and adaptively during deformation. It is formulated as convex optimization problems, and solved by harnessing sparse structures. We apply this method to tackle a challenging problem, i.e., mouse LV motion analysis using tMRI.
deformable models cardiac motion analysis sparse regularization compressed sensing