Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 28, Issue 4, Pages 535-550Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2008.2006528
Keywords
Bootstrap; high-angular resolution diffusion imaging; magnetic resonance imaging (MRI); Q-ball; white matter fiber tractography
Categories
Funding
- Biotechnology and Biological Sciences Research Council [BB/E002226/1] Funding Source: Medline
- Medical Research Council [G0300952] Funding Source: Medline
- Biotechnology and Biological Sciences Research Council [BB/E002226/1] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [GR/T02669/01] Funding Source: researchfish
- Medical Research Council [G0300952] Funding Source: researchfish
- BBSRC [BB/E002226/1] Funding Source: UKRI
- MRC [G0300952] Funding Source: UKRI
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Bootstrapping of repeated diffusion-weighted image datasets enables nonparametric quantification of the uncertainty in the inferred fiber orientation. The wild bootstrap and the residual bootstrap are model-based residual resampling methods which use a single dataset. Previously, the wild bootstrap method has been presented as an alternative to conventional bootstrapping for diffusion tensor imaging. Here we present a study of an implementation of model-based residual bootstrapping using q-ball analysis and compare the outputs with conventional bootstrapping. We show that model-based residual bootstrap q-ball generates results that closely match the output of the conventional bootstrap. Both the residual and conventional bootstrap of multi-fiber methods can be used to estimate the probability of different numbers of fiber populations existing in different brain tissues. Also, we have shown that these methods can be used to provide input for probabilistic tractography, avoiding existing limitations associated with data calibration and model selection.
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