title: Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation.
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.
authors.affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK. john@fil.ion.ucl.ac.uk
authors.forename: John
authors.initials: J
authors.lastname: Ashburner
authors.forename: Karl J
authors.initials: KJ
authors.lastname: Friston
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D005103:Expert Systems; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D017711:Nonlinear Dynamics