Journal article
Towards a comprehensive framework for movement and distortion correction of diffusion MR images: within volume movement
- Abstract:
- Most motion correction methods work by aligning a set of volumes together, or to a volume that represents a reference location. These are based on an implicit assumption that the subject remains motionless during the several seconds it takes to acquire all slices in a volume, and that any movement occurs in the brief moment between acquiring the last slice of one volume and the first slice of the next. This is clearly an approximation that can be more or less good depending on how long it takes to acquire one volume and in how rapidly the subject moves. In this paper we present a method that increases the temporal resolution of the motion correction by modelling movement as a continous function over time. This intra-volume movement correction is implemented within a previously presented framework that simultaneously estimates distortions, movement and movement-induced signal dropout. We validate the method on highly realistic simulated data containing all of these effects. It is demonstrated that we can estimate the true movement with high accuracy, and that scalar parameters derived from the data, such as fractional anisotropy, are estimated with greater fidelity when data has been corrected for intra-volume movement. Importantly, we also show that the difference in fidelity between data affected by different amounts of movement is much reduced when taking intra-volume movement into account. Finally we demonstrate a big reduction in the telltale signs of intra-volume movement in data acquired on elderly subjects.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 4.8MB, Terms of use)
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- Publisher copy:
- 10.1016/j.neuroimage.2017.02.085
Authors
+ Engineering and Physical Sciences Research Council
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- Grant:
- EP/L504889/1
- EP/L016478/1
+ National Institutes of Health
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- Grant:
- Human Connectome Project 1U54MH091657-01
- Publisher:
- Elsevier
- Journal:
- NeuroImage More from this journal
- Volume:
- 152
- Pages:
- 450–466
- Publication date:
- 2017-03-08
- Acceptance date:
- 2017-02-27
- DOI:
- EISSN:
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1095-9572
- ISSN:
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1053-8119
- Keywords:
- Pubs id:
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pubs:681943
- UUID:
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uuid:4fc99278-d485-4a6e-9023-7f4fc693e4cf
- Local pid:
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pubs:681943
- Deposit date:
-
2017-02-27
Terms of use
- Copyright holder:
- Andersson et al
- Copyright date:
- 2017
- Notes:
- © 2017 The Author(s). Published by Elsevier Inc. Open Access funded by Wellcome Trust, available under a Creative Commons license.
- Licence:
- CC Attribution (CC BY)
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