Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation

Neuroimage. 2011 Apr 1;55(3):954-67. doi: 10.1016/j.neuroimage.2010.12.049. Epub 2011 Jan 7.


This paper presents a nonlinear image registration algorithm based on the setting of Large Deformation Diffeomorphic Metric Mapping (LDDMM), but with a more efficient optimisation scheme--both in terms of memory required and the number of iterations required to reach convergence. Rather than perform a variational optimisation on a series of velocity fields, the algorithm is formulated to use a geodesic shooting procedure, so that only an initial velocity is estimated. A Gauss-Newton optimisation strategy is used to achieve faster convergence. The algorithm was evaluated using freely available manually labelled datasets, and found to compare favourably with other inter-subject registration algorithms evaluated using the same data.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Brain / anatomy & histology
  • Brain Mapping / methods*
  • Computer Simulation
  • Databases, Factual
  • Expert Systems
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods
  • Nonlinear Dynamics