Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution

By Ben Jeurissen, Alexander Leemans, Derek K. Jones, Jacques-Donald Tournier, Jan Sijbers. Human Brain Mapping, 2011

406 citations recorded by the source corpus.

Subfields: Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b4142e09fae2d581842823e4a3f8ff3958cdc4cf

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images (2012, background): “Higher order information has significantly improved the performance of tractography algorithms (Behrens et al., 2007; Jeurissen et al., 2011; Fillard et al., May, 2011).”
Denoising of diffusion MRI using random matrix theory (2016, background): “(Jeurissen et al., 2011) Some obvious spurious fibers aside, all tractograms look perfectly plausible, yet very different, especially towards the cortical areas.”
Tractography - where do we go from here? (2011, positive): “Several methods have successfully been able to model measurement noise together with the diffusion proﬁle and/or the fODFs (Behrens et al., 2003a; Berman et al., 2008; Jeurissen et al., 2011; Jones and Pierpaoli, 2005; Parker and Alexander, 2003, 2005; Sotiropoulos et al., 2010a).”
Diffusion MRI fiber tractography of the brain (2019, methodology): “Recent studies have shown that DTI‐based tractography can result in systematically unreliable and clinically misleading information, while higher‐order tractography approaches, using the same dMRI data, clearly demonstrate fiber trajectories that are biologically much more plausible.(32,36) It shoul”
Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging (2013, methodology): “One thousand residual bootstrap realizations of the entire data set were generated, using a SH model with maximum harmonic degree L ¼ 8 [Jeurissen et al., 2011].”
Dipy, a library for the analysis of diffusion MRI data (2014, methodology): “…example the ﬁber orientation distribution function (fODF) obtained by ﬁtting the CSD (constrained spherical deconvolution) model to diffusion data can be used as an estimate of the PDF, because it represents a ﬁber distribution associated with the measured diffusion signal (Jeurissen et al., 2011).”