Journal
NEUROIMAGE
Volume 122, Issue -, Pages 166-176Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2015.07.067
Keywords
Diffusion MRI; Gaussian process; Non-parametric representation; Multi-shell
Funding
- NIH Human Connectome Project [1U54MH091657-01]
- EPSRC grant [EP/L023067/1]
- Wellcome-Trust Strategic Award [098369/Z/12/Z]
- Engineering and Physical Sciences Research Council [EP/L023067/1] Funding Source: researchfish
- EPSRC [EP/L023067/1] Funding Source: UKRI
- Wellcome Trust [098369/Z/12/Z] Funding Source: Wellcome Trust
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Diffusion MRI offers great potential in studying the human brain microstructure and connectivity. However, diffusion images are marred by technical problems, such as image distortions and spurious signal loss. Correcting for these problems is non-trivial and relies on having a mechanism that predicts what to expect. In this paper we describe a novel way to represent and make predictions about diffusion MRI data. It is based on a Gaussian process on one or several spheres similar to the Geostatistical method of Kriging. We present a choice of covariance function that allows us to accurately predict the signal even from voxels with complex fibre patterns. For multi-shell data (multiple non-zero b-values) the covariance function extends across the shells which means that data from one shell is used when making predictions for another shell. (C) 2015 The Authors. Published by Elsevier Inc.
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