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

Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation.

Author information

1
Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK. john@fil.ion.ucl.ac.uk

Abstract

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.

PMID:
21216294
PMCID:
PMC3221052
DOI:
10.1016/j.neuroimage.2010.12.049
[Indexed for MEDLINE]
Free PMC Article

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