Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions

By Maxime Descoteaux, Rachid Deriche, Thomas R. Knösche, Alfred Anwander. IEEE Transactions on Medical Imaging, 2009

693 citations recorded by the source corpus.

Subfields: Computational Mathematics, Pediatrics, Perinatology and Child Health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/61eb7b453630ef09689583d25826aed928dd62b0

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In Computational Mathematics

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Citations in the corpus

Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data (2015, background): “…on Q-Ball Imaging (Tuch, 2004; Canales-Rodríguez et al., 2009; Aganj et al., 2010) or Spherical Deconvolution (SD) (Tournier et al., 2004; Alexander, 2005; Dell'Acqua et al., 2007; Descoteaux et al., 2009); for a moredetailedoverview,seeDaduccietal.”
Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging (2013, positive): “On the other hand, there are suggestions in other recent publications that voxels with multiple fiber orientations are actually commonly encountered [Descoteaux et al., 2009; Jeurissen et al., 2011].”
A hitchhiker's guide to diffusion tensor imaging (2012, background): “…even more differently oriented ﬁber bundles within the same voxel (crossing, diverging, or kissing ﬁbers) lead to incorrect estimations of ﬁber directions and pathways and abrupt terminations of tracts (Wiegell et al., 2000; Alexander et al., 2001; Barrick and Clark, 2004; Descoteaux et al., 2009).”
Diffusion Tensor Imaging and Beyond (2011, neutral): “tracks, including flow-based methods (121), graph-theoretic approaches (187), or random-walk techniques (116).”
Towards quantitative connectivity analysis: reducing tractography biases (2014, positive): “Since 81 our proposed strategies do not represent new tractography algorithms as such, we compared and applied these relax- 82 ations to the state-of-the-art fiber ODF deterministic and probabilistic algorithms (Descoteaux et al., 2009; Tournier 83 et al., 2012, 2011).”
MRtrix: Diffusion tractography in crossing fiber regions (2012, methodology): “Many of these methods have also been used to perform tractography, with improved results (Parker and Alexander, 2005; Behrens et al., 2007; Wedeen et al., 2008; Descoteaux et al., 2009; Jeurissen et al., 2011).”
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI (2013, methodology): “This has been confirmed in simulations (Descoteaux et al., 2009), in which, for example, increasing the b-value from 1000 s mm−2 to 3000 s mm−2 reduced theminimal resolvable angle between fiber bundles from about 45° to 30°, while a further increase to 5000 s mm−2 offered no further improvement.”
Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images (2012, methodology): “Spherical deconvolution is one such method, enabling the estimation of the so-called fibre orientation distribution (FOD), a continuous distribution representing the partial volume of the underlying fibres as a function of orientation (Tournier et al., 2004; Anderson, 2005; Alexander, 2005; Tournier”
Tractography - where do we go from here? (2011, neutral): “The common approach is to choose the orientation that is closest to the current direction of tracking [alternative approaches exist, e.g., (Descoteaux et al., 2009)].”
Model-based analysis of multi-shell diffusion MR data for tractography: How to get over fitting problems (2012, neutral): “However, most of the deconvolution approaches, whilst not assuming a specific form for the fODF, need to incorporate a model for the single fibre response function [31, 34–36] (i.”
Pushing the limits of in vivo diffusion MRI for the Human Connectome Project (2013, methodology): “Suchmethods are based on deconvolution techniques (Dell'Acqua et al., 2007; Descoteaux et al., 2009; Tournier et al., 2004, 2008), diffusion orientation transform (Ozarslan et al.”
A modified damped Richardson-Lucy algorithm to reduce isotropic background effects in spherical deconvolution (2010, background): “Finally, some recent tractography algorithms have begun to use the complete information of whole three-dimensional FOF profile to better characterize complex fibre organizations (Descoteaux et al., 2009; Seunarine et al., 2007).”
Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy (2013, methodology): “The adjuvant deterministic tracking approaches have been proposed to make use of multiple fiber orientations [14,15] and to obtain the connectivity matrix for human connectome studies [5,16–18].”
Conserved and variable architecture of human white matter connectivity (2011, background): “Determining the best algorithms for both deterministic and probabilistic tractography is still a growing field of important research (Behrens et al., 2007; Descoteaux et al., 2009; Sherbondy et al., 2008), and current efforts are complicated by false positives, false negatives, and biases against lo”
Diffusion MRI fiber tractography of the brain (2019, methodology): “These issues can only be addressed properly by the introduction of a suitable model for diffusion in white matter.(89,92) Nevertheless, the dODF is sometimes credited as a model‐free and thus unbiased estimator for intra‐voxel fiber orientations, making it the method of choice for fiber tracking pro”
Dipy, a library for the analysis of diffusion MRI data (2014, methodology): “You can use the SDT in the following way: from dipy.reconst.csdeconv import ConstrainedSDTModel as SdtModel sdt_model = SdtModel(gtab, ratio) Here the response function is provided as a scalar parameter ratio , which is the ratio of the smallest eigenvalue to the largest eigenvalue.”
Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution (2011, methodology): “The residual bootstrap allowed us to estimate fiber orientation uncertainty without prior assumptions about the form of uncertainty in the data, overcoming the limitations of ad hoc-methods, which assume an ad hoc relationship between the shape of FOD and the uncertainty in fiber orientation [Campbe”