Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging

By Van J. Wedeen, Patric Hagmann, W. I. Tseng, T. Reese, R. Weisskoff. Magnetic Resonance in Medicine, 2005

1,434 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/a63dcbc1e17a2ef2a100e0512ef1a71e6df01c5b

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In Pediatrics, Perinatology and Child Health

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

NTU-90: A high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction (2011, background): “…images can be acquired by using a single-shell diffusion sampling scheme, which is also known as high angular resolution diffusion image (HARDI) (Tuch et al., 2002) acquisition, or by using a grid sampling scheme, which is known as diffusion spectrum imaging (DSI) (Wedeen et al., 2005) acquisition.”
Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data (2015, methodology): “, 2006), Diffusion Spectrum Imaging (Wedeen et al., 2005) and all themethods based either on Q-Ball Imaging (Tuch, 2004; Canales-Rodríguez et al.”
Tractography - where do we go from here? (2011, neutral): “Some of the more complex diffusion modeling such as DSI (Wedeen et al., 2005), micro-structural modeling (Assaf et al., 2008), or models for ﬁber spreading require a large number of data volumes.”
Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy (2013, background): “The diffusion sampling schemes included the HARDI-253 (shell scheme) and DSI-203 (grid scheme) schemes that were previously documented in an optimization study [35].”
The human connectome: Origins and challenges (2013, background): “…been demonstrated in numerous studies, for example in studies that explicitly compared connectivity profiles derived from structural and functional connectivity data (Johansen-Berg et al., 2004), as well as by improved techniques for mapping regions with complex fiber anatomy (Wedeen et al., 2005).”
Advances in diffusion MRI acquisition and processing in the Human Connectome Project (2013, background): “Other options include sampling of multiple spheres (shells) (Assaf and Basser, 2005; Wu and Alexander, 2007) or of Cartesian grids (Wedeen et al., 2005).”
Structural connectomics in brain diseases (2013, positive): “Even though a large variety of acquisition and reconstruction schemes exists, for instance diffusion 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 vari”
Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? (2006, background): “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”
The Restless Brain (2011, neutral): “Functional fMRI has been complimented more recently by the use of diffusion tensor MRI to map, with increasing sophistication, the large ﬁber pathways in the brain (Johansen-Berg and Rushworth, 2009; Wedeen et al., 2005), resulting in more than 2000 published papers on the subject.”
Characterizing brain anatomical connections using diffusion weighted MRI and graph theory (2007, methodology): “It should be kept in mind that the DW-MRI profile is symmetric under the transformation: rYY rY.”
A modified damped Richardson-Lucy algorithm to reduce isotropic background effects in spherical deconvolution (2010, methodology): “imaging (DSI) (Wedeen et al., 2005), Q-ball imaging (Tuch, 2004), multiple compartment modelling (Behrens et al.”
Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory (2008, neutral): “…corresponded to small cubic regions of interest (ROI) covering the gray matter tissue, while arc weights were assigned according to the estimated fiber densities between nodes, which had been previously computed using an in vivo probabilistic nervous fiber tracking procedure (Wedeen et al., 2005).”
Visualization of image data from cells to organisms (2010, neutral): “Diffusion spectral imaging (DSI) 22 further increases the dimensionality with multiple acquisitions at different magnetic gradient strengths yielding a 3D dataset at each voxel in the 3D volume.”
Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution (2007, methodology): “Moreover, DSI and CHARMED both require a wide range of b-values, leading to impractical scan times.”
Dipy, a library for the analysis of diffusion MRI data (2014, methodology): “This can be used in exactly the same way as the standard DSI method: the unit vector ˆ u where r = r ˆ u . ψ DSI is a function on a sphere.”
White matter characterization with diffusional kurtosis imaging (2011, methodology): “Several techniques for assessing non-Gaussian diffusion have been developed (Alexander et al., 2002; Jensen and Helpern, 2010; Liu et al., 2004; Maier et al., 2004; Tuch, 2004; Wedeen et al., 2005).”