Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes
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 predict...
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| Format: | Article |
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Elsevier
2015
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| Online Access: | https://eprints.nottingham.ac.uk/50942/ |
| _version_ | 1848798373798215680 |
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| author | Andersson, Jesper L.R. Sotiropoulos, Stamatios N. |
| author_facet | Andersson, Jesper L.R. Sotiropoulos, Stamatios N. |
| author_sort | Andersson, Jesper L.R. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:18:45Z |
| format | Article |
| id | nottingham-50942 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:18:45Z |
| publishDate | 2015 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-509422020-05-04T17:23:04Z https://eprints.nottingham.ac.uk/50942/ Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes Andersson, Jesper L.R. Sotiropoulos, Stamatios N. 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. Elsevier 2015-11-15 Article PeerReviewed Andersson, Jesper L.R. and Sotiropoulos, Stamatios N. (2015) Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes. NeuroImage, 122 . pp. 166-176. ISSN 1053-8119 Diffusion MRI; Gaussian process; Non-parametric representation; Multi-shell https://www.sciencedirect.com/science/article/pii/S1053811915006874 doi:10.1016/j.neuroimage.2015.07.067 doi:10.1016/j.neuroimage.2015.07.067 |
| spellingShingle | Diffusion MRI; Gaussian process; Non-parametric representation; Multi-shell Andersson, Jesper L.R. Sotiropoulos, Stamatios N. Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes |
| title | Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes |
| title_full | Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes |
| title_fullStr | Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes |
| title_full_unstemmed | Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes |
| title_short | Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes |
| title_sort | non-parametric representation and prediction of single- and multi-shell diffusion-weighted mri data using gaussian processes |
| topic | Diffusion MRI; Gaussian process; Non-parametric representation; Multi-shell |
| url | https://eprints.nottingham.ac.uk/50942/ https://eprints.nottingham.ac.uk/50942/ https://eprints.nottingham.ac.uk/50942/ |