Determining and visualizing uncertainty in estimates of fiber orientation from diffusion tensor MRI

By Derek K. Jones. Magnetic Resonance in Medicine, 2003

412 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/f719d66586300949d3893a0b47c6ef9e72017429

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In Pediatrics, Perinatology and Child Health

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A modified damped Richardson-Lucy algorithm to reduce isotropic background effects in spherical deconvolution (2010, background): “Compared to DTI, where the variability refers to the orientation of first eigenvector (Jones, 2003), in the SD approach, the estimated variability may be different for each fibre orientation.”
DTI mapping of human brain connectivity: statistical fibre tracking and virtual dissection (2003, methodology): “The principal eigenvector is also very sensitive to noise, especially in areas of low anisotropy (Jones, 2003).”
Diffusion Tensor Imaging and Beyond (2011, positive): “Noise in the DW measurements will inevitably introduce uncertainty in the estimated fiber orientations, which may in turn introduce errors in the delineated pathway (153,154,157,172).”
NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain (2012, methodology): “4 but plotting the statistics of the estimates of mean orientation μ, using the 95% cone of uncertainty (Jones, 2003).”
Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T (2007, methodology): “) which result from the tensor formalism (Batchelor et al., 2003; Hasan et al., 2004; Jones, 2003; Jones and Basser, 2004).”
About “axial” and “radial” diffusivities (2009, background): “The threshold of 45° was arbitrarily chosen to represent a deviation from coherence that is well above the expected cone of uncertainty of highly anisotropic structures, previously reported as low as 3° in the splenium of the corpus callosum and 10° in the frontal white matter areas (18).”
Occipito-temporal connections in the human brain. (2003, methodology): “It has been shown that the uncertainty in ®bre orientation is spatially inhomogenous throughout the brain and, even within white matter, varies considerably from point to point (Jones, 2003).”
Motor and language DTI Fiber Tracking combined with intraoperative subcortical mapping for surgical removal of gliomas (2008, background): “The first one is technical and mainly due to inability of DTI-FT to resolve white matter architecture in areas in which more than one fiber population occupies the same voxel (Jones, 2003; Pierpaoli et al., 1996;Wiegell et al., 2000), as in presence of tumor infiltration.”
Characterization and propagation of uncertainty in diffusion‐weighted MR imaging (2003, background): “yi, M yi, M i, [ 14 ] where the parameter set now has six free parameters (, S0, d, f, , ). Each of these parameters is subject to a prior distribution, which are chosen to be noninformative except for where we ensure positivity:”
Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain (2005, methodology): “To assess the variability of the computed fiber tract orientation , we performed simulations to measure the cone of uncertainty according to Jones et al. (Jones, 2003).”
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI (2013, methodology): “using either the repeated samples bootstrap (Jones, 2003; Jones and Pierpaoli, 2005a; Pajevic and Basser, 2003), residual bootstrap (Chung et al.”
The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: A Monte Carlo study † (2004, methodology): “To characterize the distribution in the eigenvector estimates at each noise-free orientation, the 95% cone of uncertainty (CU) was computed as described elsewhere (15).”
Atlasing location, asymmetry and inter-subject variability of white matter tracts in the human brain with MR diffusion tractography (2011, positive): “Differences in the age range between studies may also explain some of the differences we found between the post-mortem (Bürgel et al., 2006) and the DTI tractography results (Jones et al., 2006; Verhoeven et al., 2010).”
Denoising of diffusion MRI using random matrix theory (2016, methodology): “In particular, we probe the variability of the principal diffusion peaks ζ i ( x ), i = 1, ···, n in voxel x by means of a coherence metric κ ( x )(Jones, 2003): with β 1 ( x ) ≥ β 2 ( x ) ≥ β 3 ( x ) the eigenvalues of the average dyadic tensor Ξ : [14] and n the number of bootstrap realizations.”