Characterization and propagation of uncertainty in diffusion‐weighted MR imaging

By Timothy Edward John Behrens, Mark William Woolrich, Mark Jenkinson, Heidi Johansen-Berg, R. Nunes, Stuart Clare, P. M. Matthews, Joanne Brady, S.M. Smith. Magnetic Resonance in Medicine, 2003

3,182 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/8ef1095dd871bfff2fda61cfd62c15513d90839f

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In Orthopedics and Sports Medicine

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Advances in diffusion MRI acquisition and processing in the Human Connectome Project (2013, methodology): “Estimating F is a deconvolution operation (Alexander, 2005; Anderson, 2005; Behrens et al., 2003; Dell'Acqua et al., 2007; Kaden et al., 2007; Sotiropoulos et al., 2012; Tournier et al., 2004; Zhang et al., 2012).”
Regularized, fast, and robust analytical Q‐ball imaging (2007, background): “[15]) T = P (BTB + λL)−1BT : R × N signal to ODF transform matrix”
A modified damped Richardson-Lucy algorithm to reduce isotropic background effects in spherical deconvolution (2010, methodology): “, 1994) and related tractography techniques (Basser et al., 2000; Behrens et al., 2003; Conturo et al., 1999; Mori et al., 1999; Parker et al., 2003) are the best known of these techniques.”
Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions (2009, methodology): “Other methods use calibration [31], [41], statistical techniques like Markov Chain Monte Carlo [35], [22], and bootstraps [42] to infer a peak uncertainty”
Hemispheric asymmetries in language-related pathways: A combined functional MRI and tractography study (2006, methodology): “…approach by incorporating the uncertainty in the orientation of the principal direction of diffusion defined for each voxel to generate maps of probability of connection to chosen start points (Behrens et al., 2003a,b; Lazar and Alexander, 2005; Parker et al., 2003; Parker and Alexander, 2003).”
Acquisition and voxelwise analysis of multi-subject diffusion data with Tract-Based Spatial Statistics (2007, methodology): “Hence, we recommend generating corrected voxelwise or cluster-based P -values using permutation testing, generating the null distribution of the maximum (across space) test statisti”
Compartment models of the diffusion MR signal in brain white matter: A taxonomy and comparison (2012, neutral): “The simplest five-parameter " BallStick " model captures the broad trends in the data better than the six-parameter DT model, performing similar to the " Bizeppelin " model.”
Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? (2006, methodology): “For example, when tracking from the cerebral peduncle or internal capsule, tractography studies tend only to find projections to the medial portion of the sensori-motor strip (e.g. Catani et al., 2002; Lazar and Alexander, 2005; Croxson et al., 2005 and many others), missing entirely the projections”
PANDA: a pipeline toolbox for analyzing brain diffusion images (2013, methodology): “Using the Markov Chain Monte Carlo sampling technique, this module estimated the local probability distribution of fiber direction at each voxel, a prerequisite for running subsequent probabilistic tractography (Behrens et al., 2003).”
Imaging brain microstructure with diffusion MRI: practicality and applications (2019, background): “The ball-and-stick model [139] represents the axons as parallel cylinders of zero radius (the “stick”), so that water diffuses only along the cylinder axis, and dispersion in the extra-axonal space follows an isotropic diffusion tensor model (the “ball”).”
Diffusion MRI at 25: Exploring brain tissue structure and function (2012, methodology): “An alternative group of techniques was developed a few years later that use probabilistic, rather than deterministic, models of diffusion (Behrens et al., 2003b; Hagmann et al., 2003; Lazar and Alexander, 2005; Parker et al., 2003).”
Diffusion tensor imaging shows white matter tracts between human auditory and visual cortex (2011, positive): “This problem can be ameliorated by probabilistic approaches (Behrens et al. 2003, 2007), which use voxel-wise probability distributions of anisotropic diffusion and estimate many possible tracts originating in the seed voxels.”
Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation (2011, methodology): “On these, tractography algorithms may be employed to derive information about the most likely fiber tract direction from particular seed or the course and strength of pathways connecting two different regions of the cortex (Behrens et al., 2003; Ramnani et al., 2004).”
Orientationally invariant indices of axon diameter and density from diffusion MRI (2010, background): “In a similar way, good estimates of multitensor models can come from single-shell HARDI protocols (Tuch et al., 2002; Parker and Alexander, 2003; Behrens et al., 2003).”
Diffusion Tensor Imaging and Beyond (2011, neutral): “Probabilistic streamlines results are, therefore, often quantified by generating maps of the number of tracks that traverse each voxel, which can then be analyzed and compared more readily (174,175).”
Blipped-Controlled Aliasing in Parallel Imaging (blipped-CAIPI) for simultaneous multi-slice EPI with reduced g-factor penalty (2011, methodology): “Furthermore, bedpost (26) was used to estimate samples from the posterior probability density function of the principal and crossing fiber orientations of both acquisitions.”
Diffusion MRI fiber tractography of the brain (2019, background): “Brain regions that contain higher densities of the resulting trajectories are then deemed to have a higher probability of “connection” with the seed point.(66,67) Probabilistic streamline results are, therefore, often quantified by generating visitation count maps of the number of trajectories that ”