Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution

By Jacques-Donald Tournier, Fernando Calamante, D. Gadian, Alan Connelly. NeuroImage, 2004

1,731 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/51cdf85a3cf191c2647899bf10462765d102b2be

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Orthopedics and Sports Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

The Human Connectome Project and Beyond: Initial Applications of 300 mT/m Gradients (2013, background): “While multidirectional ODFs and crossing fibers within the basis pontis have previously been demonstrated by other groups (Aggarwal et al., 2013; Tournier et al., 2004), the detection of multidirectional ODFs within the cuneiform/subcuneiform nucleus has not previously been reported, to our…”
How and how not to correct for CSF-contamination in diffusion MRI (2012, positive): “, 2009) - Using constrained spherical harmonic deconvolution (CSD) to extract, in each voxel, the fibre orientation density function (fODF) (Tournier et al., 2004).”
Monkey to human comparative anatomy of the frontal lobe association tracts. (2012, methodology): “A SD (Tournier et al., 2004, 2007) approach was chosen to estimate multiple orientations in voxels containing different populations of crossing fibres (Alexander, 2006).”
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., 2006) and persistent angular structure (Anderson, 2005).”
Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? (2006, methodology): “Several recent techniques (Tuch et al., 2002; Tuch et al., 2003; Jansons and Alexander, 2003; Tournier et al., 2004; Liu et al., 2004; Alexander, 2005; Hosey et al., 2005; Van Wedeen et al., 2005) have been proposed that aim to describe local diffusion within an imaging voxel without imposing the Ga”
Characterization of Cerebral White Matter Properties Using Quantitative MRI Stains (2011, methodology): “Spherical deconvolution of the propagator ODF with the expected single fiber group ODF may be used to estimate the fiber ODF, which is an estimate of the fiber orientations in a voxel (Tournier et al., 2004).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, neutral): “…important proof-of-principle, many of the classification problems investigated thus far, such as distinguishing between a given patient group and healthy controls, are practically trivial since most clinicians are able to make this distinction without the aid of sophisticated connectomic measures.”
Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution (2011, methodology): “One of these methods, constrained spherical deconvolution (CSD), is especially promising as it can offer a reliable reconstruction of ﬁber orientation distribution (FOD) functions within clinically feasible acquisition settings [Tournier et al., 2004, 2007].”
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.”
Hemispheric asymmetries in language-related pathways: A combined functional MRI and tractography study (2006, methodology): “…of diffusion (diffusivity), the directionality of the motion (anisotropy) and the principal orientation(s) of diffusion for each voxel (Jansons and Alexander, 2003; Pierpaoli et al., 1996; Pierpaoli and Basser, 1996; Tournier et al., 2004; Tuch et al., 2002, 2003; Tuch, 2004) may be calculated.”
Partial volume effect as a hidden covariate in DTI analyses (2011, background): “Similarly, these effects are not limited to the tensor framework, but also apply to other approaches of diffusion modeling, such as spherical deconvolution and Q-ball imaging (Tournier et al., 2004; Tuch, 2004), among others.”
Artifacts and pitfalls in diffusion MRI (2006, methodology): “These models include Q-space imaging (QSI) (62) and inherited diffusion spectrum imaging (DSI) (63) or Q-ball imaging (QBI) (64), generalized DTI (gDTI) (65), spherical deconvolution model (66), maximum entropy models, and persistent angular structure MRI (PASMRI) (67).”
Modeling dendrite density from magnetic resonance diffusion measurements (2007, background): “, 2004), white matter fiber tracts (Anderson, 2005; Assaf et al., 2004; Sen and Basser, 2005; Szafer et al., 1995; Tournier et al., 2004)).”
Resolving crossing fibres using constrained spherical deconvolution: Validation using diffusion-weighted imaging phantom data (2008, positive): “The spherical deconvolutionmethodworks on the principle that the DW signal originating from the various fibre populations present in the voxel add up linearly (Tournier et al., 2004).”
Model-based analysis of multi-shell diffusion MR data for tractography: How to get over fitting problems (2012, background): “(33), where the fiber response function is obtained from the most anisotropic voxels in the data and described ‘‘nonparametrically,’’ using rotational/spherical harmonics.”
Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging (2013, methodology): “To constrain the response function to an axially symmetric function, only SH coefficients with order m 1⁄4 0 were estimated [Tournier et al., 2004].”
Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions (2009, methodology): “In the synthetic simulations of [13], [23] and in the model-based approach of [48], values for the diffusion profile are set according to physiologically-inspired measurements.”