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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.

Original publication

DOI

10.1016/j.neuroimage.2015.07.067

Type

Journal article

Journal

Neuroimage

Publication Date

15/11/2015

Volume

122

Pages

166 - 176

Keywords

Diffusion MRI, Gaussian process, Multi-shell, Non-parametric representation, Artifacts, Brain, Data Interpretation, Statistical, Diffusion, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Humans, Image Processing, Computer-Assisted, Models, Neurological, Normal Distribution, Signal Processing, Computer-Assisted