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Using the model-based residual bootstrap to quantify uncertainty in fiber orientations from q-ball analysis
Haroon HA, Morris DM, Embleton K, Alexander Daniel C, Parker GJM
IEEE Transactions on Medical Imaging. 2009;28:535-550.
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Abstract
Bootstrapping of repeated diffusion-weighted imagedatasets enables non-parametric quantification of the uncertaintyin the inferred fiber orientation. The wild bootstrap and theresidual bootstrap are model-based residual resampling methodswhich use a single dataset. Previously, the wild bootstrap methodhas been presented as an alternative to conventionalbootstrapping for diffusion tensor imaging. Here we present astudy of an implementation of model-based residualbootstrapping using q-ball analysis and compare the outputs withconventional bootstrapping. We show that model-based residualbootstrap q-ball generates results that closely match the output ofthe conventional bootstrap. Both the residual and conventionalbootstrap of multi-fiber methods can be used to estimate theprobability of different numbers of fiber populations existing indifferent brain tissues. Also, we have shown that these methodscan be used to provide input for probabilistic tractography,avoiding existing limitations associated with data calibration andmodel selection.