Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation
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 perfor...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 55; no. 3; pp. 954 - 967 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.04.2011
Elsevier Limited Academic Press |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1053-8119 1095-9572 1095-9572 |
| DOI | 10.1016/j.neuroimage.2010.12.049 |
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| 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. |
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| AbstractList | 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. 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. This paper presents a nonlinear image registration algorithm based on the setting ofLarge 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. 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.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. |
| Author | Ashburner, John Friston, Karl J. |
| AuthorAffiliation | Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK |
| AuthorAffiliation_xml | – name: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK |
| Author_xml | – sequence: 1 givenname: John surname: Ashburner fullname: Ashburner, John email: john@fil.ion.ucl.ac.uk – sequence: 2 givenname: Karl J. surname: Friston fullname: Friston, Karl J. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21216294$$D View this record in MEDLINE/PubMed |
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| Copyright | 2011 Elsevier Inc. Copyright © 2011 Elsevier Inc. All rights reserved. Copyright Elsevier Limited Apr 1, 2011 2011 Elsevier Inc. 2011 Elsevier Inc. |
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| Keywords | Geodesic shooting Gauss–Newton optimisation Diffeomorphisms Shape modelling Nonlinear registration |
| Language | English |
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| Snippet | This paper presents a nonlinear image registration algorithm based on the setting of Large Deformation Diffeomorphic Metric Mapping (LDDMM), but with a more... This paper presents a nonlinear image registration algorithm based on the setting ofLarge Deformation Diffeomorphic Metric Mapping(LDDMM), but with a more... This paper presents a nonlinear image registration algorithm based on the setting of Large Deformation Diffeomorphic Metric Mapping (LDDMM), but with a more... |
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| SubjectTerms | 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 |
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| Title | Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation |
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