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Tractography gone wild: probabilistic fibre tracking using the wild bootstrap with diffusion tensor MRI.

Authors
  • Jones, Derek K
Type
Published Article
Journal
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Sep 01, 2008
Volume
27
Issue
9
Pages
1268–1274
Identifiers
DOI: 10.1109/TMI.2008.922191
PMID: 18779066
Source
Medline
License
Unknown

Abstract

Diffusion tensor magnetic resonance imaging (DT-MRI) permits the noninvasive assessment of tissue microstructure and, with fibre-tracking algorithms, allows for the 3-D trajectories of white matter fasciculi to be reconstructed noninvasively. Probabilistic algorithms allow one to assign a "confidence" to a given reconstructed pathway--but often rely on a priori assumptions about sources of uncertainty in the data. Bootstrap methods have been proposed as a way of circumventing this problem, deriving the uncertainty from the data themselves--but acquisition times for data amenable to precise and robust bootstrapping are clinically prohibitive. By combining the wild bootstrap, recently introduced to the DT-MRI literature, with tractography, we show how confidence can be assigned to reconstructed trajectories using data collected in a fraction of the time required for regular bootstrapping. We compare in vivo wild bootstrap tracking results with regular tracking results and show that results are comparable. This approach therefore allows users who have collected data sets for use with deterministic tracking algorithms, rather than those specifically designed for bootstrapping, to be able to apply bootstrap analyses and retrospectively assign confidence to their reconstructed trajectories with minimum additional effort.

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