Abstract:
: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.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Andersson JL,Sotiropoulos SNdoi
10.1016/j.neuroimage.2015.07.067subject
Has Abstractpub_date
2015-11-15 00:00:00pages
166-76eissn
1053-8119issn
1095-9572pii
S1053-8119(15)00687-4journal_volume
122pub_type
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