The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation
Open accessAbstract
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.
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Cites
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122
Self-citationAshburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507
Self-citationAVANTS, B., EPSTEIN, C., GROSSMAN, M., & GEE, J. (2008) · Medical Image Analysis · doi:10.1016/j.media.2007.06.004 · Cited by 5,669
Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later
Moler, C., & Van Loan, C. (2003) · SIAM Review · doi:10.1137/s00361445024180 · Cited by 2,357
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.037 · Cited by 2,227
Self-citationComputing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
Beg, M. F., Miller, M. I., Trouvé, A., & Younes, L. (2005) · International Journal of Computer Vision · doi:10.1023/b:visi.0000043755.93987.aa · Cited by 1,719
(1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0056181 · Cited by 1,577
Construction of a 3D probabilistic atlas of human cortical structures
Shattuck, D. W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K. L., Poldrack, R. A., Bilder, R. M., & Toga, A. W. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.031 · Cited by 1,203
Deformable templates using large deformation kinematics
Christensen, G. E., Rabbitt, R. D., & Miller, M. I. (1996) · IEEE Transactions on Image Processing · doi:10.1109/83.536892 · Cited by 1,142
Cited by
Deformable medical image registration: a survey
Sotiras, A., Davatzikos, C., & Paragios, N. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2265603 · Cited by 1,587
“The Gauss-Newton optimizer has been used in [81, 236, 245].”
MethodAdvancing Research and Treatment for Frontotemporal Lobar Degeneration (ARTFL) investigators, Thijssen, E. H., La Joie, R., Wolf, A., Strom, A., Wang, P., Iaccarino, L., Bourakova, V., Cobigo, Y., Heuer, H., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0762-2 · Cited by 761
Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging
Horn, A., Li, N., Dembek, T. A., Kappel, A., Boulay, C., Ewert, S., Tietze, A., Husch, A., Perera, T., Neumann, W.-J., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.068 · Cited by 739
Quicksilver: Fast predictive image registration – A deep learning approach
Yang, X., Kwitt, R., Styner, M., & Niethammer, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.008 · Cited by 630
“For example, Ashburner and Friston [6] use a Gauss–Newton method to accelerate convergence for LDDMM and Zhang et al. [7] propose a finite-dimensional approximation of LDDMM, achieving a roughly 25 speed-up over a standard LDDMM optimization-based solution.”
MethodPatterns of gray matter atrophy in genetic frontotemporal dementia: results from the GENFI study
Cash, D. M., Bocchetta, M., Thomas, D. L., Dick, K. M., van Swieten, J. C., Borroni, B., Galimberti, D., Masellis, M., Tartaglia, M. C., Rowe, J. B., et al. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.10.008 · Cited by 200
Metadata
- Authors
- Ashburner, J., & Friston, K. J.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-04
- Volume
- 55
- Issue
- 3
- Pages
- 954-967
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2024524081
- Cited by
- 503
- References
- 34
- FWCI
- 12.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- creativecommons.org/licenses/by/3.0/ (vor, 837 d)
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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