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A fast diffeomorphic image registration algorithm
Abstract
This paper describes DARTEL, which is an algorithm for diffeomorphic image registration. It is implemented for both 2D and 3D image registration and has been formulated to include an option for estimating inverse consistent deformations. Nonlinear registration is considered as a local optimisation problem, which is solved using a Levenberg-Marquardt strategy. The necessary matrix solutions are obtained in reasonable time using a multigrid method. A constant Eulerian velocity framework is used, which allows a rapid scaling and squaring method to be used in the computations. DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects.
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Lateral orbitofrontal gray matter abnormalities in subjects with problematic smartphone use
Lee, D., Namkoong, K., Lee, J., Lee, B. O., & Jung, Y.-C. (2019) · Journal of Behavioral Addictions · doi:10.1556/2006.8.2019.50 · Cited by 64
“We used diffeomorphic anatomical registration with an exponentiated Lie algebra algorithm (DARTEL) to increase the accuracy of intersubject alignment by modeling the shape of each brain using a host of parameters (Ashburner, 2007).”
MethodSynaptic density in healthy human aging is not influenced by age or sex: a 11C-UCB-J PET study
Michiels, L., Delva, A., van Aalst, J., Ceccarini, J., Vandenberghe, W., Vandenbulcke, M., Koole, M., Lemmens, R., & Laere, K. V. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117877 · Cited by 59
A VBM study demonstrating ‘apparent’' effects of a single dose of medication on T1-weighted MRIs
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Associations between early adrenarche, affective brain function and mental health in children
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Temporal irreversibility of large-scale brain dynamics in Alzheimer's disease
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Decreased Premotor Cortex Volume in Victims of Urban Violence with Posttraumatic Stress Disorder
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Metadata
- Authors
- Ashburner, J.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Ashburner, John
- ORCID
- Ashburner, John
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2007-10
- Volume
- 38
- Issue
- 1
- Pages
- 95-113
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2155298532
- Cited by
- 8,122
- References
- 35
- Citations by year
- 2026: 343 · 2025: 522 · 2024: 530 · 2023: 514 · 2022: 620 · 2021: 681 · 2020: 630 · 2019: 555 · 2018: 528 · 2017: 541 · 2016: 481 · 2015: 539 · 2014: 466 · 2013: 428 · 2012: 295
- FWCI
- 96.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust
- Universiteit Maastricht
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- diffeomorphic registration, DARTEL, nonlinear registration, Levenberg-Marquardt algorithm, multigrid methods, scaling and squaring, whole-brain MRI, brain shape, sex classification, age prediction
- MeSH terms
- Algorithms, Artificial Intelligence, Brain, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Pattern Recognition, Automated, Sensitivity and Specificity, Subtraction Technique, Reproducibility of Results, Imaging, Three-Dimensional
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