The B‐matrix must be rotated when correcting for subject motion in DTI data

By Alexander Leemans, Derek K. Jones. Magnetic Resonance in Medicine, 2009

1,376 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/112c62f79dfff532a873e9bc1737789b46c7e597

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In Pediatrics, Perinatology and Child Health

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution (2011, methodology): “Motion distortion correction was applied taking into account the B-matrix rotation [Leemans and Jones, 2009] and the tensor model was ﬁtted to the data using a weighted (anisotropic covariance matrix) linear regression method [Basser et al., 1994a].”
Short frontal lobe connections of the human brain. (2012, methodology): “All tracts presented were dissected on both hemispheres.”
Spurious group differences due to head motion in a diffusion MRI study (2013, background): “, 2002) and reoriented the corresponding diffusion-weighting gradient vectors accordingly (Rohde et al., 2004; Leemans and Jones, 2009).”
Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls (2013, methodology): “The DWdatawere corrected for motion and eddy currents, including signal modulation and b-matrix rotation (Leemans and Jones, 2009; Leemans et al., 2009).”
Characterization of Cerebral White Matter Properties Using Quantitative MRI Stains (2011, background): “It has been reported that corrections to the encoding directions after motion and eddy current distortion correction can lead to significant changes in the estimated diffusion tensor measures (Leemans and Jones, 2009).”
An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging (2016, methodology): “The rotation parameters from estimated movement is sometimes used to rotate the diffusion gradients prior to subsequent diffusion modeling (Leemans and Jones, 2009).”
Model-based analysis of multi-shell diffusion MR data for tractography: How to get over fitting problems (2012, background): “The gradient orientations were rotated to account for the rigid component of these transformations (30).”
Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images (2012, methodology): “Finally, the computed rigid transformations were applied to realign all DW volumes using cubic b-spline interpolation, followed by reorientation of the gradient directions (Leemans and Jones, 2009).”
Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging (2013, methodology): “For each subject, all DW scans were concatenated (not averaged) into a single data set and corrected for subject motion and residual eddy current induced geometric distortions with the required B-matrix adjustments [Leemans and Jones, 2009], resulting in a total of 720 DW and 72 b 1⁄4 0 s/mm(2) imag”
Diffusion Tensor Imaging and Beyond (2011, neutral): “5b) the same DW images, but now corrected for subject motion and eddy-current induced geometric distortions (64).”
Image processing and analysis methods for the Adolescent Brain Cognitive Development Study (2018, positive): “The diffusion gradient matrix is adjusted for head rotation, important for accurate model fitting and tractography (Hagler et al., 2009; Leemans and Jones, 2009).”
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI (2013, background): “degrees to estimates of the principal eigenvector, or peaks in the fODF or dODF (Leemans and Jones, 2009).”
The hubs of the human connectome are generally implicated in the anatomy of brain disorders (2014, methodology): “Image preprocessing included correction for eddy currents and subject motion, normalization into standardized space using an appropriate B-matrix rotation on the diffusion gradients (Leemans and Jones, 2009), before robust estimation of the tensor model (RESTORE) (Chang et al., 2005).”
PANDA: a pipeline toolbox for analyzing brain diffusion images (2013, methodology): “Finally, the gradient direction of each DWI volume was rotated according to the resultant affine transformations (Leemans and Jones, 2009).”
How and how not to correct for CSF-contamination in diffusion MRI (2012, positive): “…non-diffusion weighted volume, using normalised mutual information as the cost-function, followed by appropriate re-orientation of the encoding vectors (Leemans and Jones, 2009) andmodulation of the signal intensity by the Jacobian determinant of the transformation (Jones and Cercignani, 2010).”
Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data (2014, positive): “Correction included the required B-matrix adjustments (Leemans and Jones, 2009) and appropriate modulation of the DW images with the Jacobian of the transformation matrix (Jones and Cercignani, 2010).”