Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation

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• 作者：John Ashburner ; ^a ; ^{john@fil.ion.ucl.ac.uk} ; Karl J. Friston^a
• 关键词：Diffeomorphisms ; Geodesic shooting ; Shape modelling ; Nonlinear registration ; Gauss– ; Newton optimisation
• 刊名：NeuroImage
• 出版年：2011
• 出版时间：1 April 2011
• 年：2011
• 卷：55
• 期：3
• 页码：954-967
• 全文大小：1950 K

文摘

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.