Journal article
Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images
- Abstract:
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Despite its great potential in studying brain anatomy and structure, diffusion magnetic resonance imaging (dMRI) is marred by artefacts more than any other commonly used MRI technique. In this paper we present a non-parametric framework for detecting and correcting dMRI outliers (signal loss) caused by subject motion. Signal loss (dropout) affecting a whole slice, or a large connected region of a slice, is frequently observed in diffusion weighted images, leading to a set of unusable measurem...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Accepted manuscript, pdf, 2.7MB)
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- Publisher copy:
- 10.1016/j.neuroimage.2016.06.058
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Authors
Funding
+ National Institutes of Health
More from this funder
Grant:
Human Connectome Project 1U54MH091657-01
+ Engineering and Physical Sciences Research Council
More from this funder
Grant:
EP/L023067/1
EP/L016478/1
EP/L504889/1
Bibliographic Details
- Publisher:
- Elsevier Publisher's website
- Journal:
- Neuroimage Journal website
- Volume:
- 141
- Pages:
- 556-572
- Publication date:
- 2016-07-05
- Acceptance date:
- 2016-06-30
- DOI:
- EISSN:
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1095-9572
- ISSN:
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1053-8119
- Source identifiers:
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632204
Item Description
- Keywords:
- Pubs id:
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pubs:632204
- UUID:
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uuid:05d76de8-0b2d-454a-8b33-80bd7879344e
- Local pid:
- pubs:632204
- Deposit date:
- 2016-07-06
Terms of use
- Copyright holder:
- Elsevier
- Copyright date:
- 2016
- Notes:
- © 2016 Elsevier Inc. This is the accepted manuscript version of the article. The final version is available online from Elsevier at: [10.1016/j.neuroimage.2016.06.058].
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