Characterization of anisotropy in high angular resolution diffusion‐weighted MRI

By L. Frank. Magnetic Resonance in Medicine, 2002

623 citations recorded by the source corpus.

The methods of group theory are applied to the problem of characterizing the diffusion measured in high angular resolution MR experiments. This leads to a natural representation of the local diffusion in terms of spherical harmonics. In this representation, it is shown that isotropic diffusion, anisotropic diffusion from a single fiber, and anisotropic diffusion from multiple fiber directions fall into distinct and separable channels. This decomposition can be determined for any voxel without any prior information by a spherical harmonic transform, and for special cases the magnitude and orientation of the local diffusion may be determined. Moreover, non‐diffusion–related asymmetries produced by experimental artifacts fall into channels distinct from the fiber channels, thereby allowing their separation and a subsequent reduction in noise from the reconstructed fibers. In the case of a single fiber, the method reduces identically to the standard diffusion tensor method. The method is applied to normal volunteer brain data collected with a stimulated echo spiral high angular resolution diffusion‐weighted (HARD) acquisition. Magn Reson Med 47:1083–1099, 2002. Published 2002 Wiley‐Liss, Inc.

Subfields: Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b1046cef47fa3f9755e33d10848f365371f376d8

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution (2007, background): “Unfortunately, the diffusion tensor model is not always adequate, particularly in voxels containing contributions from differently oriented fibre bundles (Alexander et al., 2002; Frank, 2002; Tuch et al., 2002; Behrens et al., 2007).”
A framework for a streamline‐based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements (2003, background): “This ADC profile (21,40) is a possible candidate for a PDF (with the probability P(k̂) being proportional to d(k̂)) for connectivity analyses, and is based solely on D (i.”
Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain (2005, neutral): “Y. Assaf, P.J. Basser / NeuroImage 27 (2005) 48 – 58 53 et al., 2002; Assaf et al., 2000; Frank, 2002; Tuch et al., 2003; Wedeen et al., 2000).”
Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging (2002, background): “ical harmonics [29], [30], may be incorporated into the definition of .”
Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue (2005, methodology): “The test functions we use in the simulations are mixtures of Gaussian densities, which model the displacement density expected in the presence of single, two and three fibre populations (Alexander et al. 2001; Frank 2002 ).”
Basic principles of diffusion-weighted imaging. (2003, background): “A novel approach to this problem is to measure the diffusion coefficient at high angular resolution [56] in order to more accurately detect variations in diffusion along different directions.”
Dipy, a library for the analysis of diffusion MRI data (2014, methodology): “Due to their practical mathematical properties, there have been several proposals using spherical harmonics to describe the apparent diffusion coefﬁcient (ADC) computed from HARDI, starting from the work of Frank (2001, 2002) and Alexander et al. (2002) and Tuch et al. (2002).”
Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data (2014, neutral): “However, themodel is invalid in regions containingmultiple tracts that cross or pass very close to each other (Alexander et al., 2001, 2002; Frank, 2001, 2002; Tuch et al., 2002).”
Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution (2011, methodology): “However, in voxels containing multiple ﬁber orientations, this model has been shown to be inadequate [Alexander et al., 2002; Frank, 2001, 2002; Tuch et al., 2002].”
“Squashing peanuts and smashing pumpkins”: How noise distorts diffusion‐weighted MR data (2004, neutral): “1 2 0 1cos B + 2sin(2) 0B = ln(0), b T [9]”
Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T (2007, background): “This difference should be sensitive to b-value (the higher the b-value, the more pronounced the effect Frank, 2002).”
Regularized, fast, and robust analytical Q‐ball imaging (2007, methodology): “[8].”
A review of diffusion tensor imaging studies in schizophrenia. (2007, background): “Moreover, higher angular data can be used to solve the problem of fiber crossings, and to guide fiber tracking in areas where the anisotropy is low, and where uncertainty exists with respect to the major direction of diffusion (Tuch et al., 2002; Frank, 2002).”
Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT) (2006, background): “Several studies proposed to represent the diffusivity profile using a spherical harmonic expansion (Frank, 2002; Alexander et al., 2002).”
Diffusion Tensor Imaging and Beyond (2011, neutral): “Crossing fibers are even more problematic for tensorbased tractography methods (62,90,95,101,103,104): if one corrupt orientation estimate is encountered, the tracking algorithm may venture off course into an adjacent white matter structure, leading to both false-positive and false-negatives connect”
DTI mapping of human brain connectivity: statistical fibre tracking and virtual dissection (2003, methodology): “The family of tracking algorithms that we could globally describe as deterministic reduce the tensorto a vector-field (Conturo et al., 1999; Mori et al., 1999; Jones et al., 1999; Basser et al., 2000; Tench et al., 2002) and consequently do not take into account the uncertainty of the fibre directio”