Geometric metamorphosis

  • Authors:

  • Marc Niethammer;Gabriel L. Hart;Danielle F. Pace;Paul M. Vespa;Andrei Irimia;John D. Van Horn;Stephen R. Aylward

  • Affiliations:

  • University of North Carolina, Chapel Hill NC and Biomedical Research Imaging Center, UNC Chapel Hill NC;Kitware, Inc., Carrboro NC;Kitware, Inc., Carrboro NC;Brain Injury Research Center, University of California, Los Angeles CA;Laboratory of Neuro Imaging, University of California, Los Angeles CA;Laboratory of Neuro Imaging, University of California, Los Angeles CA;Kitware, Inc., Carrboro NC

  • Venue:
  • MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
  • Year:
  • 2011

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Abstract

Standard image registration methods do not account for changes in image appearance. Hence, metamorphosis approaches have been developed which jointly estimate a space deformation and a change in image appearance to construct a spatio-temporal trajectory smoothly transforming a source to a target image. For standard metamorphosis, geometric changes are not explicitly modeled. We propose a geometric metamorphosis formulation, which explains changes in image appearance by a global deformation, a deformation of a geometric model, and an image composition model. This work is motivated by the clinical challenge of predicting the long-term effects of traumatic brain injuries based on time-series images. This work is also applicable to the quantification of tumor progression (e.g., estimating its infiltrating and displacing components) and predicting chronic blood perfusion changes after stroke. We demonstrate the utility of the method using simulated data as well as scans from a clinical traumatic brain injury patient.