2011
DOI: 10.1016/j.neuroimage.2010.12.049
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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 u… Show more

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Cited by 379 publications
(388 citation statements)
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References 30 publications
(53 reference statements)
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“…One of the major concerns is about the different approaches used for the preprocessing and analysis of data, which can give rise to different results (Ashburner & Friston, 2011) and thus could contribute toward explaining the inconsistencies in the field of bilingualism. Tables 1-2 summarize the preprocessing and analysis of the studies included in this review.…”
Section: Methodsological Concerns and Recommendationsmentioning
confidence: 99%
“…One of the major concerns is about the different approaches used for the preprocessing and analysis of data, which can give rise to different results (Ashburner & Friston, 2011) and thus could contribute toward explaining the inconsistencies in the field of bilingualism. Tables 1-2 summarize the preprocessing and analysis of the studies included in this review.…”
Section: Methodsological Concerns and Recommendationsmentioning
confidence: 99%
“…The baseline GM, WM and CSF segmentations were then used to generate a group average template using the diffeomorphic warping ‘ Shoot ’ algorithm in SPM12 21. The segmentations were then warped using the resulting deformation fields, modulated with the Jacobian determinant data and smoothed with a 6 mm full-width half-maximum (FWHM) Gaussian kernel.…”
Section: Methodsmentioning
confidence: 99%
“…The study‐specific template is then normalized to MNI space in step 1(c). Two deformations, the flow field images representing individual image to study‐specific template and the transformations of the template to MNI space, were combined and encoded in modulated images.SHOOT ST is the same as option 2 except that the two implementations of DARTEL were replaced by a diffeomorphic registration using geodesic shooting and Gauss‐Newton optimization22 (Supp. Fig.…”
Section: Methodsmentioning
confidence: 99%