Memory Efficient LDDMM for Lung CT

Thomas Polzin*, Marc Niethammer, Mattias P. Heinrich, Heinz Handels, Jan Modersitzki

*Corresponding author for this work
15 Citations (Scopus)


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 DIRLab chronic obstructive pulmonary disease (COPD) lung CT data sets obtaining a mean landmark distance after registration of 1.03mm and the best average results so far.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsLeo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin
Number of pages9
PublisherSpringer International Publishing
Publication date02.10.2016
ISBN (Print)978-3-319-46725-2
ISBN (Electronic)978-3-319-46726-9
Publication statusPublished - 02.10.2016
Event19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2016
- Athens, Greece
Duration: 17.10.201621.10.2016


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