Memory Efficient LDDMM for Lung CT

  • Thomas PolzinEmail author
  • Marc Niethammer
  • Mattias P. Heinrich
  • Heinz Handels
  • Jan Modersitzki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)


In this paper a novel Large Deformation Diffeomorphic Metric Mapping (LDDMM) scheme is presented which has significantly lower computational and memory demands than standard LDDMM but achieves the same accuracy. We exploit the smoothness of velocities and transformations by using a coarser discretization compared to the image resolution. This reduces required memory and accelerates numerical optimization as well as solution of transport equations. Accuracy is essentially unchanged as the mismatch of transformed moving and fixed image is incorporated into the model at high resolution. Reductions in memory consumption and runtime are demonstrated for registration of lung CT images. State-of-the-art accuracy is shown for the challenging DIR-Lab chronic obstructive pulmonary disease (COPD) lung CT data sets obtaining a mean landmark distance after registration of 1.03 mm and the best average results so far.



This work was supported by a fellowship of the German Academic Exchange Service (DAAD) and NSF grant ECCS-1148870.

Supplementary material

432173_1_En_4_MOESM1_ESM.pdf (75 kb)
Supplementary material 1 (pdf 75 KB)


  1. 1.
    Ashburner, J., Friston, K.J.: Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. NeuroImage 55(3), 954–967 (2011)CrossRefGoogle Scholar
  2. 2.
    Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. IJCV 61(2), 139–157 (2005)CrossRefGoogle Scholar
  3. 3.
    Castillo, E., Castillo, R., Fuentes, D., Guerrero, T.: Computing global minimizers to a constrained B-spline image registration problem from optimal l1 perturbations to block match data. Med. Phys. 41(4), 041904 (2014)CrossRefGoogle Scholar
  4. 4.
    Castillo, E., Castillo, R., Martinez, J., Shenoy, M., Guerrero, T.: Four-dimensional deformable image registration using trajectory modeling. Phys. Med. Biol. 55(1), 305–327 (2010)CrossRefGoogle Scholar
  5. 5.
    Castillo, R., Castillo, E., Fuentes, D., Ahmad, M., Wood, A.M., et al.: A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive. Phys. Med. Biol. 58(9), 2861–2877 (2013)CrossRefGoogle Scholar
  6. 6.
    Galbán, C.J., Han, M.K., Boes, J.L., Chughtai, K., Meyer, C.R., et al.: Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat. Med. 18, 1711–1715 (2012)CrossRefGoogle Scholar
  7. 7.
    Hart, G.L., Zach, C., Niethammer, M.: An optimal control approach for deformable registration. In: IEEE CVPR Workshops, pp. 9–16 (2009)Google Scholar
  8. 8.
    Heinrich, M.P., Handels, H., Simpson, I.J.A.: Estimating large lung motion in COPD patients by symmetric regularised correspondence fields. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 338–345. Springer, Heidelberg (2015).  10.1007/978-3-319-24571-3_41CrossRefGoogle Scholar
  9. 9.
    König, L., Rühaak, J.: A fast and accurate parallel algorithm for non-linear image registration using normalized gradient fields. In: IEEE ISBI, pp. 580–583 (2014)Google Scholar
  10. 10.
    Lassen, B., Kuhnigk, J.M., Schmidt, M., Krass, S., Peitgen, H.O.: Lung and lung lobe segmentation methods at Fraunhofer MEVIS. In: Proceedings of the Fourth International Workshop on Pulmonary Image Analysis, pp. 185–199 (2011)Google Scholar
  11. 11.
    Modersitzki, J.: FAIR: Flexible Algorithms for Image Registration. SIAM (2009)Google Scholar
  12. 12.
    Murphy, K., van Ginneken, B., Reinhardt, J.M., Kabus, S., Ding, K., et al.: Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE Trans. Med. Imaging 30(11), 1901–1920 (2011)CrossRefGoogle Scholar
  13. 13.
    Nocedal, J., Wright, S.: Numerical Optimization. Springer, New York (2006)zbMATHGoogle Scholar
  14. 14.
    Regan, E.A., Hokanson, J.E., Murphy, J.R., Lynch, D.A., Beaty, T.H., et al.: Genetic Epidemiology of COPD (COPDGene) study design. COPD 7, 32–43 (2011)CrossRefGoogle Scholar
  15. 15.
    Risser, L., Vialard, F.X., Baluwala, H.Y., Schnabel, J.A.: Piecewise-diffeomorphic image registration: application to the motion estimation between 3D CT lung images with sliding conditions. Med. Image Anal. 17(2), 182–193 (2013)CrossRefGoogle Scholar
  16. 16.
    Rühaak, J., Heldmann, S., Kipshagen, T., Fischer, B.: Highly accurate fast lung CT registration. In: SPIE 2013, Medical Imaging, p. 86690Y-1-9 (2013)Google Scholar
  17. 17.
    Sakamoto, R., Mori, S., Miller, M.I., Okada, T., Togashi, K.: Detection of time-varying structures by large deformation diffeomorphic metric mapping to aid reading of high-resolution CT images of the lung. PLoS ONE 9(1), 1–11 (2014)Google Scholar
  18. 18.
    Vialard, F.X., Risser, L., Rueckert, D., Cotter, C.J.: Diffeomorphic 3D image registration via geodesic shooting using an efficient adjoint calculation. IJCV 97(2), 229–241 (2012)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Zhang, M., Fletcher, P.T.: Finite-dimensional Lie Algebras for fast diffeomorphic image registration. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 249–260. Springer, Heidelberg (2015).  10.1007/978-3-319-19992-4_19CrossRefGoogle Scholar

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Authors and Affiliations

  • Thomas Polzin
    • 1
    Email author
  • Marc Niethammer
    • 2
  • Mattias P. Heinrich
    • 3
  • Heinz Handels
    • 3
  • Jan Modersitzki
    • 1
    • 4
  1. 1.Institute of Mathematics and Image ComputingUniversity of LübeckLübeckGermany
  2. 2.Department of Computer Science and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Institute of Medical InformaticsUniversity of LübeckLübeckGermany
  4. 4.Fraunhofer MEVISLübeckGermany

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