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Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images

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...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Publisher copy:
10.1016/j.neuroimage.2016.06.058

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Institution:
University of Oxford
Department:
Oxford, MSD, Clinical Neurosciences
Role:
Author
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Institution:
University of Oxford
Department:
Oxford, MSD, Clinical Neurosciences
Role:
Author
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Institution:
University of Oxford
Department:
Oxford, MSD, Psychiatry
Role:
Author
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Grant:
Human Connectome Project 1U54MH091657-01
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

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