Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration

@article{Rhaak2017EstimationOL,
  title={Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration},
  author={Jan R{\"u}haak and T. Polzin and S. Heldmann and Ivor J. A. Simpson and H. Handels and J. Modersitzki and M. Heinrich},
  journal={IEEE Transactions on Medical Imaging},
  year={2017},
  volume={36},
  pages={1746-1757}
}
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… Expand
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