Regularization is used in deformable image registration to encourage plausible displacement fields, and significantly impacts the derived correspondences. Sliding motion, such as that between the lungs and chest wall and between the abdominal organs, …
Existing atlas-building methods for di usion-tensor images are not designed for longitudinal data. This paper proposes a novel longitudinal atlas-building framework explicitly accounting for temporal dependencies of longitudinal MRI data. …
We propose a new deformable medical image registration method that uses a physically-based simulator and an iterative optimizer to estimate the simulation parameters determining the deformation field between the two images. Although a …
Respiratory motion challenges lung radiation therapy with uncertainties of the location of important anatomical structures in the thorax. To capture the trajectory of the motion, dense image matching methods and learning-based motion prediction …
This paper addresses large-displacement-diffeomorphic mapping registration from an optimal control perspective. This viewpoint leads to two complementary formulations. One approach requires the explicit computation of coordinate maps, whereas the …
This paper discusses an optimal control approach for the registration of image time-series (growth modeling). It combines and augments work on an optimal control formulation to optical flow with theory from large-displacement diffeomorphic image …