Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation

• Published in: NeuroImage (Orlando, Fla.) Vol. 55; no. 3; pp. 954 - 967
• Main Authors: Ashburner, John, Friston, Karl J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2011; Elsevier Limited; Academic Press
• Subjects: Accuracy; Algorithms; Behavior; Brain - anatomy & histology; Brain Mapping - methods; Computer Simulation; Databases, Factual; Deformation; Diffeomorphisms; Expert Systems; Gauss–Newton optimisation; Geodesic shooting; Humans; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Nonlinear Dynamics; Nonlinear registration; Registration; Shape modelling; Studies; Technical Note; Geodesic shooting; Gauss–Newton optimisation; Diffeomorphisms; Shape modelling; Nonlinear registration
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2010.12.049

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.