Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images
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...
| Main Authors: | , , , |
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| Format: | Journal article |
| Published: |
Elsevier
2016
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