Estimation of large motion in lung CT by integrating regularized keypoint correspondences into dense deformable registration

Rühaak, Jan, Polzin, Thomas, Heldmann, Stefan, Simpson, Ivor J A, Handels, Heinz, Modersitzki, Jan and Heinrich, Mattias P (2017) Estimation of large motion in lung CT by integrating regularized keypoint correspondences into dense deformable registration. IEEE Transactions on Medical Imaging, 36 (8). pp. 1746-1757. ISSN 0278-0062

[img] PDF - Published Version
Restricted to SRO admin only

Download (5MB)


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.

Item Type: Article
Keywords: computerised tomography;image registration;lung;medical image processing;motion compensation;optimisation;pneumodynamics;dense deformable registration;pulmonary CT scan registration;large respiratory motion estimation;sparse keypoint correspondence integration;dense continuous optimization framework;curvature regularization mechanism;volume change control mechanism;parallel matrix-free derivative calculation scheme;time 5 min;distance 0.82 mm;Lungs;Optimization;Computed tomography;Robustness;Jacobian matrices;Image edge detection;Radio frequency;Computed tomography;COPD;image registration;Jacobian determinant;keypoints;lung;Markov random fields;Algorithms;Humans;Lung;Motion;Tomography, X-Ray Computed
Schools and Departments: School of Engineering and Informatics > Informatics
Research Centres and Groups: Data Science Research Group
Depositing User: Ivor Simpson
Date Deposited: 24 Jan 2020 11:36
Last Modified: 28 Jan 2021 16:45

View download statistics for this item

📧 Request an update