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Deformation Based Morphometry Analysis of Serial Magnetic Resonance Images of Mouse Brains

  • Satheesh Maheswaran
  • Hervé Barjat
  • Simon Bate
  • Thomas Hartkens
  • Derek L. G. Hill
  • Michael F. James
  • Lorna Tilling
  • Neil Upton
  • Jo Hajnal
  • Daniel Rueckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4057)

Abstract

Deformation based morphometry is used to detect differences in in-vivo Magnetic Resonance Image (MRI) of the mouse brain obtained from two transgenic strains: TASTPM mice that over-express proteins associated with Alzheimer’s disease, and wild-type mice. MRI was carried out at four time points. We compare two different methods to detect group differences in the longitudinal and cross-sectional data. Both methods are based on non-rigid registration of the images to a mouse brain atlas. The whole brain volume measurements on 27 TASTPM and wild-type animals are reproducible to within 0.4% of whole brain volume. The agreement between different methods for measuring volumes in a serial study is shown. The ability to quantify changes in growth between strains in whole brain, hippocampus and cerebral cortex is demonstrated.

Keywords

Mouse Brain Brain Volume Registration Method Wildtype Mouse Serial Magnetic Resonance Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Satheesh Maheswaran
    • 1
  • Hervé Barjat
    • 4
  • Simon Bate
    • 4
  • Thomas Hartkens
    • 3
  • Derek L. G. Hill
    • 3
  • Michael F. James
    • 4
  • Lorna Tilling
    • 4
  • Neil Upton
    • 4
  • Jo Hajnal
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Dept. of ComputingSouth Kensington Campus, Imperial CollegeLondon
  2. 2.Imaging Sciences DepartmentImperial College, Hammersmith HospitalLondon
  3. 3.IXICO LtdLondon
  4. 4.Neurology and GI Center of Excellence for Drug DiscoveryHarlow

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