  We present a novel algorithm for the registration of pulmonary CT scans. Our
method is designed for large respiratory motion by integrating sparse keypoint
correspondences into a dense continuous optimization framework. The detection
of keypoint correspondences enables robustness against large deformations by
jointly optimizing over a large number of potential discrete displacements,
whereas the dense continuous registration achieves subvoxel alignment with
smooth transformations. Both steps are driven by the same normalized gradient
fields data term. We employ curvature regularization and a volume change
control mechanism to prevent foldings of the deformation grid and restrict the
determinant of the Jacobian to physiologically meaningful values. Keypoint
correspondences are integrated into the dense registration by a quadratic
penalty with adaptively determined weight. Using a parallel matrix-free
derivative calculation scheme, a runtime of about 5 min was realized on a
standard PC. The proposed algorithm ranks first in the EMPIRE10 challenge on
pulmonary image registration. Moreover, it achieves an average landmark
distance of 0.82 mm on the DIR-Lab COPD database, thereby improving upon the
state of the art in accuracy by 15%. Our algorithm is the first to reach the
inter-observer variability in landmark annotation on this dataset.
