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TRActs constrained by UnderLying INfant anatomy (TRACULInA): An automated probabilistic tractography tool with anatomical priors for use in the newborn brain.
- Source :
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NeuroImage . Oct2019, Vol. 199, p1-17. 17p. - Publication Year :
- 2019
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Abstract
- The ongoing myelination of white-matter fiber bundles plays a significant role in brain development. However, reliable and consistent identification of these bundles from infant brain MRIs is often challenging due to inherently low diffusion anisotropy, as well as motion and other artifacts. In this paper we introduce a new tool for automated probabilistic tractography specifically designed for newborn infants. Our tool incorporates prior information about the anatomical neighborhood of white-matter pathways from a training data set. In our experiments, we evaluate this tool on data from both full-term and prematurely born infants and demonstrate that it can reconstruct known white-matter tracts in both groups robustly, even in the presence of differences between the training set and study subjects. Additionally, we evaluate it on a publicly available large data set of healthy term infants (UNC Early Brain Development Program). This paves the way for performing a host of sophisticated analyses in newborns that we have previously implemented for the adult brain, such as pointwise analysis along tracts and longitudinal analysis, in both health and disease. • Tool successfully reconstructs 14 WM pathways in both full- and pre-term subjects. • The accuracy of the reconstruction depends on the test but not on the training data. • No significant effect of segmentation/registration on our pathway reconstruction. • Training set may combine data from different studies (or different from test data). • Diffusion values extracted with manual and automated methods are closely associated. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10538119
- Volume :
- 199
- Database :
- Academic Search Index
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- 137891650
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2019.05.051