Lung motion estimation by robust point matching and spatiotemporal tracking for 4D CT

We propose a deformable registration approach to estimate patient-specific lung motion during free breathing for four-dimensional (4D) computed tomography (CT) based on point matching and tracking between images in different phases. First, a robust point matching (RPM) algorithm coarsely aligns the source phase image onto all other target phase images of 4D CT. Scale-invariant feature transform (SIFT) is introduced into the cost function in order to accelerate and stabilize the convergence of the point matching. Next, the temporal consistency of the estimated lung motion model is preserved by fitting the trajectories of the points in the respiratory phase using L-1 norm regularization. Then, the fitted positions of a point along the trajectory are used as the initial positions for the point tracking. Spatial mean-shift iteration is employed to track points in all phase images. The tracked positions in all phases are used to perform RPM again. These steps are repeated until the number of updated points is smaller than a given threshold a. With this method, the correspondence between the source phase image and other target phase image is established more accurately. Trajectory fitting ensures the estimated trajectory does not fluctuate violently. We evaluated our method by using the public DIR-lab, POPI-model, CREATIS and COPDgene lung datasets. In the experimental results, the proposed method achieved satisfied accuracy for image registration. Our method also preserved the topology of the deformation fields well for image registration with large deformation.