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J. Imaging 2018, 4(1), 8; https://doi.org/10.3390/jimaging4010008

Reference Tracts and Generative Models for Brain White Matter Tractography

1
Department of Neuroimaging Sciences and Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH16 4SB, UK
2
Department of Psychology and Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK
3
Department of Neuroimaging Sciences, Centre for Cognitive Ageing and Cognitive Epidemiology and UK Dementia Research Institute at the University of Edinburgh, University of Edinburgh, Edinburgh EH16 4SB, UK
4
UCL Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
This paper is an extended version of our paper published in Annual Conference on Medical Image Understanding and Analysis, Edinburgh, UK, 11–13 July 2017.
*
Author to whom correspondence should be addressed.
Received: 3 November 2017 / Revised: 13 December 2017 / Accepted: 26 December 2017 / Published: 28 December 2017
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
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Abstract

Background: Probabilistic neighborhood tractography aims to automatically segment brain white matter tracts from diffusion magnetic resonance imaging (dMRI) data in different individuals. It uses reference tracts as priors for the shape and length of the tract, and matching models that describe typical deviations from these. We evaluated new reference tracts and matching models derived from dMRI data acquired from 80 healthy volunteers, aged 25–64 years. Methods: The new reference tracts and models were tested in 50 healthy older people, aged 71.8 ± 0.4 years. The matching models were further assessed by sampling and visualizing synthetic tracts derived from them. Results: We found that data-generated reference tracts improved the success rate of automatic white matter tract segmentations. We observed an increased rate of visually acceptable tracts, and decreased variation in quantitative parameters when using this approach. Sampling from the matching models demonstrated their quality, independently of the testing data. Conclusions: We have improved the automatic segmentation of brain white matter tracts, and demonstrated that matching models can be successfully transferred to novel data. In many cases, this will bypass the need for training data and make the use of probabilistic neighborhood tractography in small testing datasets newly practicable. View Full-Text
Keywords: MRI; brain; white matter; unsupervised segmentation; tractography MRI; brain; white matter; unsupervised segmentation; tractography
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Muñoz Maniega, S.; Bastin, M.E.; Deary, I.J.; Wardlaw, J.M.; Clayden, J.D. Reference Tracts and Generative Models for Brain White Matter Tractography. J. Imaging 2018, 4, 8.

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