Quantitative Diffusion-Tensor Anisotropy Brain MR Imaging: Normative Human Data and Anatomic Analysis

Shimony, Joshua S.; McKinstry, Robert C.; Akbudak, Erbil; Aronovitz, Joseph A.; Snyder, Abraham Z.; Lori, Nicolás F.; Cull, Thomas S.; Conturo, Thomas E.

doi:10.1148/radiology.212.3.r99au51770

Cited by 286 publications

(199 citation statements)

References 34 publications

Supporting

Mentioning

186

Contrasting

Unclassified

Order By: Relevance

“…The degree to which the probability distribution is direction dependent is given by the anisotropy A calculated from the elements of D without diagonalization. 32,47 The A is similar to the relative anisotropy (RA) of Pierpaoli and Basser,57 except that A is on a standard scale from 0 to 1, 32,47 while RA is on a scale from 0 to 2 p . 47,58 Computation of WM ®ber bundle trajectories using DT-MRI data…”

Section: Creation Of the Diffusion Tensor And Images Of Its Derived Pmentioning

confidence: 73%

“…Tetrahedral-orthogonal data were acquired 32,47 using a pulse sequence designed and implemented for continuous acquisition of multislice DT-MRI data with multiple scan repeats. 3 The tetrahedralorthogonal configuration has favorable encoding directions for tensor measurements, 47 and has low condition number and noise 40,48 at a modest number of directional encodings. A spatially balanced arrangement of four noncolinear tetrahedral gradients 32 was chosen to reduce the effect of sample background gradients and the accumulation of Eddy currents.…”

Section: Experimental Methods For Human Dtt Studies Acquisition Of Dtmentioning

confidence: 99%

“…The data pass through sequential unsupervised processing steps. Images are realigned using a combination of intra-modal 49 and cross-modal 2D and 3D affine procedures specially targeted to low-order linear distortions 47 that are characteristic of Eddy currents. 50 The realignment procedures also correct for in-plane and through-plane subject motion.…”

Section: Processing Of Dt-mri Data Prior To Tensor and Track Computationmentioning

confidence: 99%

“…Such association fibers are important in cognitive neuroscience studies, and are challenging to track due to low anisotropy. 47 The standard helical radius of the WM pathway (measured from the straight axis of the large cylinder to the curved central axis of the tube) is 10.7 mm. The standard inclination angle a between the tangent of the helical trajectory and the transverse plane is 10°(or a pitch of 11.8 mm, where pitch is the distance along Z for a full turn of the helix as defined in p. 8 of Cantor et al 62 ).…”

Section: Simulation Of Dtt Errors Under Differ-ent Conditionsmentioning

confidence: 99%

“…The diffusion parameters of the standard and variant tissues are from Shimony et al 47 In all cases, the WM fibers (and e e e 1 ) ran parallel to the helical axis. The orientation angles (f, , ) of the local direction of e e e 1 are given by 32 and Hsu et al, 38 where R(f, , ) is a rotation matrix and Ã is the reduced (diagonalized) tensor.…”

Section: Simulation Of Dtt Errors Under Differ-ent Conditionsmentioning

confidence: 99%

See 4 more Smart Citations

Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results

et al. 2002

View full text Add to dashboard Cite

We present a description, biological results and a reliability analysis for the method of diffusion tensor tracking (DTT) of white matter fiber pathways. In DTT, diffusion-tensor MRI (DT-MRI) data are collected and processed to visualize the line trajectories of fiber bundles within white matter (WM) pathways of living humans. A detailed description of the data acquisition is given. Technical aspects and experimental results are illustrated for the geniculo-calcarine tract with broad projections to visual cortex, occipital and parietal U-fibers, and the temporocalcarine ventral pathway. To better understand sources of error and to optimize the method, accuracy and precision were analyzed by computer simulations. In the simulations, noisy DT-MRI data were computed that would be obtained for a WM pathway having a helical trajectory passing through gray matter. The error vector between the real and ideal track was computed, and random errors accumulated with the square root of track length consistent with a random-walk process. Random error was most dependent on signal-to-noise ratio, followed by number of averages, pathway anisotropy and voxel size, in decreasing order. Systematic error only occurred for a few conditions, and was most dependent on the stepping algorithm, anisotropy of the surrounding tissue, and non-equal voxel dimensions. Both random and systematic errors were typically below the voxel dimension. Other effects such as track rebound and track recovery also depended on experimental conditions. The methods, biological results and error analysis herein may improve the understanding and optimization of DTT for use in various applications in neuroscience and medicine.

show abstract

Section: Creation Of the Diffusion Tensor And Images Of Its Derived Pmentioning

confidence: 73%

Section: Experimental Methods For Human Dtt Studies Acquisition Of Dtmentioning

confidence: 99%

Section: Processing Of Dt-mri Data Prior To Tensor and Track Computationmentioning

confidence: 99%

Section: Simulation Of Dtt Errors Under Differ-ent Conditionsmentioning

confidence: 99%

Section: Simulation Of Dtt Errors Under Differ-ent Conditionsmentioning

confidence: 99%

See 3 more Smart Citations

Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results

et al. 2002

View full text Add to dashboard Cite

show abstract

A geometric analysis of diffusion tensor measurements of the human brain

et al. 2000

View full text Add to dashboard Cite

Apparent diffusion tensor measurements in myelin-deficient rat spinal cords

et al. 2001

View full text Add to dashboard Cite

The apparent diffusion tensor (ADT) was measured in excised and fixed spinal cords from myelin‐deficient (md) rats and age‐matched controls. These data were used to obtain the principal diffusivities of the ADT, and also the scalar invariant parameters D̄ (averaged principal diffusivity) and Aσ (anisotropy index) for four white matter and two gray matter regions. The results for white matter regions showed that the principal diffusivities were significantly higher for md animals, and while the D̄ was increased in tissue from md animals, the Aσ was found to be decreased. Grey matter D̄ was measured to be between those of white matter from control and md animals, and the Aσ was much smaller than that of white matter from both sets of animals, indicating that diffusion in md white matter is more anisotropic than in gray matter. The results show that while myelination is not a prerequisite for diffusion anisotropy, it does influence the magnitude of the observed anisotropy. Magn Reson Med 45:191–195, 2001. © 2001 Wiley‐Liss, Inc.

show abstract

Quantitative Diffusion-Tensor Anisotropy Brain MR Imaging: Normative Human Data and Anatomic Analysis

Abstract: Quantitative diffusion anisotropy images can be obtained rapidly and demonstrate subtle WM anatomy. Different histologic types of WM have significant and reproducible anisotropy differences.

Cited by 286 publications

References 34 publications

Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results

Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results

A geometric analysis of diffusion tensor measurements of the human brain

Apparent diffusion tensor measurements in myelin-deficient rat spinal cords

Contact Info

Product

Resources

About