- Abstract:
-
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...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted manuscript
- Files:
-
-
(pdf, 2.7MB)
-
- Publisher copy:
- 10.1016/j.neuroimage.2016.06.058
-
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- Grant:
- Human Connectome Project 1U54MH091657-01
- Grant:
- EP/L023067/1; EP/L504889/1; EP/L016478/1
- 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:
-
1095-9572
- ISSN:
-
1053-8119
- URN:
-
uuid:05d76de8-0b2d-454a-8b33-80bd7879344e
- Source identifiers:
-
632204
- Local pid:
- pubs:632204
- Keywords:
- 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].
Journal article
Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images
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+ National Institutes of Health
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+ Engineering and Physical Sciences Research Council
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