A Comparative Study of Acquisition Schemes for Diffusion Tensor Imaging Using MRI

Papadakis, N.; Xing, Da; Huang, Christopher L.‐H.; Hall, Laurance D.; Carpenter, T. Adrian

doi:10.1006/jmre.1998.1673

Cited by 193 publications

(179 citation statements)

References 30 publications

(78 reference statements)

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“…This metric quantifies the influence of measurement noise and diffusion gradient scheme upon the tensor calculation. When the measurement noise dependence is not considered (for example, by making the assumption of unity noise), then the ⌺ bD metric reduces to the index introduced by Papadakis et al (21). However, the ⌺ bD metric has additional utility beyond in optimizing and evaluating diffusion gradient schemes for SNR as well as diffusion gradient scheme.…”

Section: Discussionmentioning

confidence: 99%

“…The variation of diffusion anisotropy measurements has been reported to be dependent on choice of anisotropy index (18 -20). To minimize that variation, Jones et al (6) suggested using gradient orientations derived from a model of electrostatic repulsion on the surface of a sphere, whereas Papadakis et al (21), Skare et al (22), and Cao (25) introduced parameters that could be minimized for an optimal diffusion-weighting scheme. Simulations by Papadakis et al (23) suggested that a minimum number of gradient directions (N Ϸ 20) was necessary.…”

Section: Magnetic Resonance (Mr) Diffusion Tensor Imaging (Dti) Is a mentioning

confidence: 99%

“…These were used to calculate the RA and FA using Eqs. [21] and [22], and the mean anisotropy error for each index using Eqs. [23] and [24].…”

Section: Application 1: Comparison Of Dti Acquisition Protocolsmentioning

confidence: 99%

“…Using the analytical formulism, the sum of the dimensionless tensor variances were computed for five selected diffusion-weighting gradient schemes (5,6,21,22,24,25,33) as described in Table 3. For comparison, the condition number proposed by Skare et al (22) was also computed for each of these schemes.…”

Section: Application 2: Evaluation Of Diffusion-weighting Schemesmentioning

confidence: 99%

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Analytical error propagation in diffusion anisotropy calculations

Poonawalla

Zhou

2004

Magnetic Resonance Imaging

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Purpose:To develop an analytical formalism describing how noise and selection of diffusion-weighting scheme propagate through the diffusion tensor imaging (DTI) computational chain into variances of the diffusion tensor elements, and errors in the relative anisotropy (RA) and fractional anisotropy (FA) indices. Materials and Methods:Singular-value decomposition (SVD) was used to determine the tensor variances, with diffusion-weighting scheme and measurement noise incorporated into the design matrix. Anisotropy errors were then derived using propagation of error. To illustrate the applications of the model, 12 data sets were acquired from each human subject, over a range of b-values (500 -2500 seconds/mm 2 ) and diffusion-weighting gradient directions (N ϭ 6 -55). The mean RA and FA values and their respective errors were calculated within a region of interest (ROI) in the splenium. The RA and FA errors as a function of b-value and N were evaluated, and a number of diffusion-weighting schemes were assessed based on a new metric, sum of diffusion tensor variances.Results: When the acquisition time was held constant, the sum of the diffusion tensor variances decreased as N increased. The same trend was also observed for several diffusion-weighting schemes with constant condition number when noise in the diffusion-weighted (DW) images was assumed unity. Errors in both FA and RA increased with b-value and decreased with N. The FA error in the splenium was approximately threefold smaller than RA error, irrespective of b-value or N. Conclusion:The condition number may not adequately characterize the noise sensitivity for a given diffusionweighting scheme. Signal averaging may not be as effective as increasing N, especially when N is small (e.g., N Ͻ 13). Due to its smaller error, FA is preferred over RA for quantitative DTI applications.

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Section: Discussionmentioning

confidence: 99%

Section: Magnetic Resonance (Mr) Diffusion Tensor Imaging (Dti) Is a mentioning

confidence: 99%

“…These were used to calculate the RA and FA using Eqs. [21] and [22], and the mean anisotropy error for each index using Eqs. [23] and [24].…”

Section: Application 1: Comparison Of Dti Acquisition Protocolsmentioning

confidence: 99%

Section: Application 2: Evaluation Of Diffusion-weighting Schemesmentioning

confidence: 99%

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Analytical error propagation in diffusion anisotropy calculations

Poonawalla

Zhou

2004

Magnetic Resonance Imaging

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“…The advantage of this method is that it could simplify the analysis of DWI data. In new singleshot acquisition schemes that acquire DWIs with gradients applied in many directions, 74,86 this strategy has the disadvantage that twice as many DWIs would have to be acquired. Moreover, an additional parameter is required per voxel as a free parameter in the estimation of the diffusion tensor to determine the cross-terms due to the imaging gradients alone.…”

Section: Hardware Issuesmentioning

confidence: 99%

Diffusion‐tensor MRI: theory, experimental design and data analysis – a technical review

2002

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This article treats the theoretical underpinnings of diffusion-tensor magnetic resonance imaging (DT-MRI), as well as experimental design and data analysis issues. We review the mathematical model underlying DT-MRI, discuss the quantitative parameters that are derived from the measured effective diffusion tensor, and describe artifacts thet arise in typical DT-MRI acquisitions. We also discuss difficulties in identifying appropriate models to describe water diffusion in heterogeneous tissues, as well as in interpreting experimental data obtained in such issues. Finally, we describe new statistical methods that have been developed to analyse DT-MRI data, and their potential uses in clinical and multi-site studies.

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