Diffusion tensor imaging of in vivo and excised rat spinal cord at 7 T with an icosahedral encoding scheme

Madi, Saaussan; Hasan, Khader M.; Narayana, Ponnada A.

doi:10.1002/mrm.20304

Cited by 88 publications

(125 citation statements)

References 36 publications

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“…In Figure 1C, after retrospective correction using the total effective scaling vector [Eq. (9)], the overestimation of MD was minimized, and the patterns of the FA map and colormap were similar to the originals in Figure 1A.…”

Section: Computer Simulationmentioning

confidence: 70%

“…However, although inseparable, both gradient errors can be simultaneously corrected with opposite diffusion weighting gradients or by the total effective scaling vector [Eq. (9)] as long as the calibration protocol has the same imaging parameters as the DTI experiments. One limitation of this method is that it assumes that the residual gradients that arise from the diffusion-weighting gradients are along the same axis.…”

Section: Discussionmentioning

confidence: 99%

“…By combining the total effective factors of 3 physical and corresponding antipodal directions (±x, ±y, and ±z), the total effective scaling vector becomes a 6-element vector: (9) Accurate measurements of the waveforms of residual gradients, background gradients, and the associate functions (R 1 or O 1 ) are not required to estimate c effR and c effO . These factors are estimated directly using the results of regression analyses: β 1 and β c1 [see Eqs.…”

Section: Total Effective Scaling Vector and Retrospective Correction mentioning

confidence: 99%

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A Method for Calibrating Diffusion Gradients in Diffusion Tensor Imaging

Wu¹,

Alexander²

2007

Journal of Computer Assisted Tomography

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Objective-To calibrate and correct the gradient errors including gradient amplitude scaling errors, background/imaging gradients, and residual gradients in diffusion tensor imaging (DTI).Methods-A calibration protocol using an isotropic phantom was proposed. Gradient errors were estimated by using linear regression analyses on quadratic functions of diffusion gradients along 3 orthogonal directions. A 6-element total effective scaling vector is generated from the calibration protocol to retrospectively correct gradient errors in DTI experiments.Results-The accuracy of the calibration protocol was within 1% or less in estimating gradient scaling errors. On both the brain study and the computer simulations, the retrospective correction minimized undesirable estimate biases of DTI measurements due to gradient errors. Conclusion-The protocol and retrospective correction are shown to be effective. The method may be used for prospective correction if actual diffusion-gradient waveforms are available. The methodology is expandable to general diffusion imaging schemes.

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Section: Computer Simulationmentioning

confidence: 70%

Section: Discussionmentioning

confidence: 99%

Section: Total Effective Scaling Vector and Retrospective Correction mentioning

confidence: 99%

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A Method for Calibrating Diffusion Gradients in Diffusion Tensor Imaging

Wu¹,

Alexander²

2007

Journal of Computer Assisted Tomography

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“…The theoretical formalism presented above was validated using water phantom DTI measurements (GE spherical water phantom at scanner room regulated room temperature) as described previously at 1.5 T [17,18], 3.0 T [6,21,23], and 7.0 T [24] scanners. For DT-MRI data acquisitions at both 1.5 T and 3.0 T, the T E was 60-90 ms, and T R was 6000-10000 ms depending on the selected b-factor and slice coverage.…”

Section: Methodsmentioning

confidence: 99%

A framework for quality control and parameter optimization in diffusion tensor imaging: theoretical analysis and validation

Hasan

2007

Magnetic Resonance Imaging

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In this Communication, a theoretical framework for quality control and parameter optimization in diffusion tensor imaging (DTI) is presented and validated. The approach is based on the analytical error propagation of the mean diffusivity (D av ) obtained directly from the diffusion-weighted data (DW) acquired using rotationally-invariant and uniformly distributed icosahedral encoding schemes. The error propagation of a recently described and validated cylindrical tensor model is further extrapolated to the spherical tensor case (diffusion anisotropy ~ 0) to relate analytically the precision error in fractional tensor anisotropy (FA) with the mean diffusion signal-to-noise ratio (DNR). The approach provided simple analytical and empirical quality control measures for optimization of diffusion parameter space in an isotropic medium that can be tested using widely available water phantoms.

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“…Due to respiratory gating, the repetition time (TR * 1.2 sec) was slightly varied among animals according to the period of individual breathing cycle (*270 msec), with fixed echo time (TE) at 38 msec. Images, three slices per breath, were obtained with diffusion-sensitizing gradients applied in six orientations: (Gx,Gy,Gz) ¼ (1,1,0), (1,0,1), (0,1,1), (-1,1,0) , represents optimal experimental conditions within the gradient performance of our system for diffusion imaging of rodent model spinal cord (Budde et al, 2008;Hasan and Narayana, 2006;Kim et al, 2006Kim et al, , 2007Loy et al, 2007;Madi et al, 2005). Eight scans were averaged per k-space line, with field of view 10Â10 mm 2 , and data matrix 128Â256 (zero filled to 256Â256).…”

Section: In-vivo Diffusion Tensor Imagingmentioning

confidence: 99%

Diffusion Tensor Imaging at 3 Hours after Traumatic Spinal Cord Injury Predicts Long-Term Locomotor Recovery

Kim

Loy

Wang

et al. 2010

Journal of Neurotrauma

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Accurate diagnosis of spinal cord injury (SCI) severity must be achieved before highly aggressive experimental therapies can be tested responsibly in the early phases after trauma. These studies demonstrate for the first time that axial diffusivity (ljj), derived from diffusion tensor imaging (DTI) within 3 h after SCI, accurately predicts long-term locomotor behavioral recovery in mice. Female C57BL=6 mice underwent sham laminectomy or graded contusive spinal cord injuries at the T9 vertebral level (5 groups, n ¼ 8 for each group). In-vivo DTI examinations were performed immediately after SCI. Longitudinal measurements of hindlimb locomotor recovery were obtained using the Basso mouse scale (BMS). Injured and spared regions of ventrolateral white matter (VLWM) were reliably separated in the hyperacute phase by threshold segmentation. Measurements of ljj were compared with histology in the hyperacute phase and 14 days after injury. The spared normal VLWM determined by hyperacute ljj and 14-day histology correlated well (r ¼ 0.95). A strong correlation between hindlimb locomotor function recovery and ljj-determined spared normal VLWM was also observed. The odds of significant locomotor recovery increased by 18% with each 1% increase in normal VLWM measured in the hyperacute phase (odds ratio ¼ 1.18, p ¼ 0.037). The capability of measuring subclinical changes in spinal cord physiology and murine genetic advantages offer an early window into the basic mechanisms of SCI that was not previously possible. Although significant obstacles must still be overcome to derive similar data in human patients, the path to clinical translation is foreseeable and achievable.

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Diffusion tensor imaging of in vivo and excised rat spinal cord at 7 T with an icosahedral encoding scheme

Cited by 88 publications

References 36 publications

A Method for Calibrating Diffusion Gradients in Diffusion Tensor Imaging

A Method for Calibrating Diffusion Gradients in Diffusion Tensor Imaging

A framework for quality control and parameter optimization in diffusion tensor imaging: theoretical analysis and validation

Diffusion Tensor Imaging at 3 Hours after Traumatic Spinal Cord Injury Predicts Long-Term Locomotor Recovery

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