Lung deformation estimation using spatially mean shift for 4D-CT

Lung motion estimation plays an important role in image-guided radiotherapy. This paper presents an algorithm to estimate lung deformation and motion during respiration using 4D deformable image registration for 4D-CT data. The image intensity of 4D-CT is transformed into vessel feature space, which is used to extract and track bifurcation points of lungs vessel. The spatially mean shift iteration is used to track bifurcation points in all phase images. The initial positions of these bifurcation points in all phase images are provided by the initial transformations between the reference image and all phase images estimated by matching candidate corresponding bifurcation points based on SIFT features. The trajectory of tracked bifurcation points are used to construct lung deformable model by TPS interpolation. We have validated our proposed algorithm on DIR-lab dataset. The results are compared with the results of the previously 4D registration methods, showing improvement of registration error and significant improvement of computation time for 4D deformable image registration.