Reference Tracts and Generative Models for Brain White Matter Tractography

• Main Authors: Susana Muñoz Maniega, Mark E. Bastin, Ian J. Deary, Joanna M. Wardlaw, Jonathan D. Clayden
• Format: Article
• Language: English
• Published: MDPI AG 2017-12-01
• Series: Journal of Imaging
• Subjects: MRI; brain; white matter; unsupervised segmentation; tractography
• Online Access: https://www.mdpi.com/2313-433X/4/1/8

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.