Optimised diffusion-weighting for measurement of apparent diffusion coefficient (ADC) in human brain

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

doi:10.1016/s0730-725x(97)00037-4

Cited by 171 publications

(186 citation statements)

References 22 publications

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“…The ADC map of each patient was generated between T2-EPI and trace images using the 2-point method. 9 ROIs created on trace images were copied and pasted onto the ADC map using thè`c opy-to-temp W copy-from-temp'' function included in the MR apparatus, and the ADC of each tumor was measured. The ADCs obtained were compared between the 2 diŠerent DWIs using Wilcoxon's signed rank test.…”

Section: Imaging Analysismentioning

confidence: 99%

The Effect of Simultaneous Use of Respiratory Triggering in Diffusion-weighted Imaging of the Liver

et al. 2006

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Objective: To determine the eŠect of simultaneous use of respiratory triggering in hepatic diŠusion-weighted imaging (DWI), we compared DWI with respiratory triggering (RT-DWI) and DWI under free breathing (FB-DWI) in terms of relative contrast between hepatic tumor and surrounding liver parenchyma, apparent diŠusion coe‹cient (ADC) measurement, and frequency of respiratory misregistration. Materials and Methods: Thirty patients (21 men, 9 women, aged 25 to 80 years) with liver metastasis or hepatocellular carcinoma in the right hepatic lobe were examined with RT-and FB-DWI. In patients having multiple tumors, up to 3 lesions were selected by mutual agreement of 2 diagnostic radiologists. Finally, 59 nodules were selected for evaluation. Relative contrast ratio (RCR) between the lesions and surrounding parenchyma and ADC were measured in each hepatic lesion in each sequence. The diŠerences in RCR and ADC between RT-and FB-DWI were statistically analyzed using Wilcoxon's signed rank test. The frequency of respiratory misregistration advents in each sequence was visually evaluated by the 2 diagnostic radiologists and assessed using McNemar's test.Results: RCRs were signiˆcantly higher in RT-than in FB-DWI (Pº0.001). The diŠer-ence in ADC between the 2 sequences was not signiˆcant. However, ADCs tended to be more scattered in FB-than in RT-DWI. The frequency of respiratory misregistration advents was signiˆcantly higher in FB-than in RT-DWI.Conclusion: FB-was inferior to RT-DWI in both contrast between tumors and surrounding liver parenchyma and in accuracy of ADC measurement as far as the numbers of excitation in each sequence were the same. For preoperative examination of hepatic resection, RT-is more suitable than FB-DWI.

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Section: Imaging Analysismentioning

confidence: 99%

The Effect of Simultaneous Use of Respiratory Triggering in Diffusion-weighted Imaging of the Liver

et al. 2006

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“…Specifically, the precision of diffusion anisotropy calculations as a function of the key data acquisition parameters (b-value and N) has not been fully characterized, although a number of authors have investigated the noise sensitivity at individual stages in the diffusion tensor computational chain. For example, Jones et al (6) and Xing et al (15) have investigated optimization of the b-values in order to maximize the signal-to-noise ratio (SNR) of the raw DW images. Armitage and Bastin (16) generalized this approach to maximizing the SNR of the diffusion trace map, termed diffusion-to-noise ratio (DNR).…”

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

confidence: 99%

“…The tensor shape invariants were then calculated at each voxel according to Eqs. [15][16][17] and their variances according to Eqs. [18 -20].…”

Section: Application 1: Comparison Of Dti Acquisition Protocolsmentioning

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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“…DTI involves the acquisition of large data sets that require the selection of an immense parameter space which has not been clearly investigated with analytical approaches [4][5][6][7][8]. In general, the optimization of the single tensor multi-dimensional parameter problem remains a challenging complex multi-dimensional problem [1,[4][5][6][7][8][9][10][11][12][13][14][15]. The DT-MRI of the entire brain or a certain tissue volume-of-interest is usually sought in some clinically affordable time, T, and a repetition time, T R .…”

Section: Introductionmentioning

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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Optimised diffusion-weighting for measurement of apparent diffusion coefficient (ADC) in human brain

Cited by 171 publications

References 22 publications

The Effect of Simultaneous Use of Respiratory Triggering in Diffusion-weighted Imaging of the Liver

The Effect of Simultaneous Use of Respiratory Triggering in Diffusion-weighted Imaging of the Liver

Analytical error propagation in diffusion anisotropy calculations

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

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