Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution

By Jacques-Donald Tournier, Fernando Calamante, Alan Connelly. NeuroImage, 2007

2,278 citations recorded by the source corpus.

Subfields: Orthopedics and Sports Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/18cfee92095c476376b798736f0a038838d833af

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In Orthopedics and Sports Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information (2012, methodology): “For the high b-value data set, both deterministic and probabilistic streamlines algorithms were tested using the FODs derived from CSD.”
Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data (2014, positive): “The WM constraint matrix evaluates the amplitudes of the WM ODF along a predefined set of 300 uniformly distributed orientations (Tournier et al., 2007).”
Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images (2012, methodology): “We used the group average response function to perform CSD for each individual subject.”
Global fiber reconstruction becomes practical (2011, methodology): “(2009), the fiber orientation distributions are estimated by constrained spherical deconvolution (Tournier et al., 2007) together with an adaptive anisotropic Gaussian filter.”
Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging (2013, methodology): “The single-fiber response function corresponds to the DW signal that would be expected for an ideal fiber population aligned along the z-axis and was estimated from the data themselves using a previously published approach [Tournier et al., 2004, 2007].”
Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution (2011, methodology): “Recent studies, using both simulations and phantom data, have shown that CSD is able to resolve narrow interﬁber angles [Tournier et al., 2007, 2008].”
The Virtual Epileptic Patient: Individualized whole‐brain models of epilepsy spread (2017, methodology): “, 2010), and MRtrix (Smith et al., 2013, 2012; Tournier et al., 2007) to process T1 and dMRI scans.”
Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy (2013, result): “We further compared the QA-aided tractography with the tractography obtained from constrained spherical deconvolution (CSD)[37].”
Diffusion Tensor Imaging and Beyond (2011, positive): “100,000 tracks were produced by seeding at random throughout the brain, with a probabilistic streamlines algorithm using fiber orientation distributions estimated using constrained spherical deconvolution (99), based on the same data as shown in Fig.”
Structural connectomics in brain diseases (2013, positive): “…tensor imaging (DTI)(Basser et al., 1994), diffusion spectrum imaging (DSI)(Wedeen et al., 2005), single shell HARDI schemes (Aganj et al., 2010; Tournier et al., 2007; Tuch et al., 2003), and a variety of tractography methods have been proposed (Fillard et al., 2011), currently DTI and…”
Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited (2014, methodology): “C in the case of ROI-PCG, the optimal specificity and sensitivity are observed at 60° for DTI and Q-ball but at 45° for CSD.”
Model-based analysis of multi-shell diffusion MR data for tractography: How to get over fitting problems (2012, methodology): “An alternative approach is to estimate the fODF directly from the data using deconvolution [31–33].”
Resolving crossing fibres using constrained spherical deconvolution: Validation using diffusion-weighted imaging phantom data (2008, positive): “We investigated a range of b-values and crossing angles, as previous simulationworks (including our own) have indicated a dependence of performance on both of these parameters (Tournier et al., 2007; Zhan and Yang, 2006).”