Memory Efficient LDDMM for Lung CT

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

Abstract

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.

Supplementary material

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

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Thomas Polzin
    • 1
  • 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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