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The effect of different number of diffusion gradients on SNR of diffusion tensor-derived measurement maps

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DOI: 10.4236/jbise.2009.22018    5,940 Downloads   11,169 Views   Citations

ABSTRACT

Diffusion tensor imaging (DTI) is mainly applied to white matter fiber tracking in human brain, but there is still a debate on how many diffusion gradient directions should be used to get the best results. In this paper, the performance of 7 protocols corresponding to 6, 9, 12, 15, 20, 25, and 30 noncollinear number of diffusion gradi-ent directions (NDGD) were discussed by com-paring signal-noise ratio (SNR) of tensor- de-rived measurement maps and fractional ani-sotropy (FA) values. All DTI data (eight healthy volunteers) were downloaded from the website of Johns Hopkins Medical Institute Laboratory of Brain Anatomi-cal MRI with permission. FA, apparent diffusion constant mean (ADC-mean), the largest eigen-value (LEV), and eigenvector orientation (EVO) maps associated with LEV of all subjects were calculated derived from tensor in the 7 proto-cols via DTI Studio. A method to estimate the variance was presented to calculate SNR of these tensor-derived maps. Mean ± standard deviation of the SNR and FA values within re-gion of interest (ROI) selected in the white mat-ter were compared among the 7 protocols. The SNR were improved significantly with NDGD increasing from 6 to 20 (P<0.05). From 20 to 30, SNR were improved significantly for LEV and EVO maps (P<0.05), but no significant dif-ferences for FA and ADC-mean maps (P>0.05). There were no significant variances in FA val-ues within ROI between any two protocols (P> 0.05). The SNR could be improved with NDGD in-creasing, but an optimum protocol is needed because of clinical limitations.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Zhang, N. , Deng, Z. , Wang, F. and Wang, X. (2009) The effect of different number of diffusion gradients on SNR of diffusion tensor-derived measurement maps. Journal of Biomedical Science and Engineering, 2, 96-101. doi: 10.4236/jbise.2009.22018.

References

[1] M. Jackowski, C. Y. Kao, M. L. Qiu, et al. (2005) White matter tractography by anisotropic wavefront evolution and diffusion tensor imaging. Medical Image Analysis, 9, 427–440.
[2] T. McGraw, B.C. Vemuri, Y. Chen, et al. (2004) DT-MRI de-noising and neuronal fiber tracking. Medical Image Analysis, 8, 95–111.
[3] D. L. Bihan, J. F. Mangin, C. Poupon, et al. (2001) Diffusion Tensor Imaging: Concepts and Applications. Journal of Mag-netic Resonance Imaging, 13, 534–546.
[4] P. J. Basser, J. Mattiello, D. LeBihan. (1994) Estimation of the effective selfdiffusion tensor from theNMRspin echo. JMagn Reson B, 103, 247–254.
[5] P. J. Basser, J. Mattiello, D. LeBihan. (1994) MR diffusion tensor spectroscopy and imaging. Biophys J, 66, 259–267.
[6] H. Jiang, P. C.M. van Zijl, et al. (2006) DtiStudio: Resource program for diffusion tensor computation and fiber bundle tracking. computer methods and programs in biomedicine, 8 1,106–116.
[7] D. S. Tuch. (2004) Q-Ball Imaging. Magnetic Resonance in Medicine, 52, 1358–1372.
[8] N. G. Papadakis, D. Xing, G. C. Houston, et al. (1999) A study of rotational invariant and symmetric indices of diffusion ani-sotropy. Magn Reson Imaging, 17, 881–92.
[9] S. Skare, M. Hedehus, M.E. Moseley, et al. (2000) Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with MRI. J Magn Reson, 147, 340–52.
[10] D. K. Jones. (2004) The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study. Magn Reson Med, 51, 807–15.
[11] H.Ni, V.Kavcic. T. Zhu, et al. (2006) Effects of Number of Diffusion Gradient Directions on Derived Diffusion Tensor Im-aging Indices in Human Brain. AJNR Am J Neuroradiol, 27,1776-81.
[12] A. H. Poonawalla, MS, X. H Joe Zhou, PhD *. (2004) Analyti-cal error propagation in diffusion anisotropy calculations. JMRI, 19, 489–498.
[13] M. Zou (2001) Deconvolution and Signal recovery. Publishing Company of National Defence and Industry (Chinese book).
[14] S Pajevic, C Pierpaoli. (1999) Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: ap-plication to white matter fiber tract mapping in the human brain. Magn Reson Med, 42, 526–540.
[15] D.K. Jones, M.A. Horsfield. (1999) A. Simmons. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn. Reson. Med, 42 (3), 515–525.
[16] N.G. Papadakis, C. D. Murrills, L. D. Hall, et al. (2000) Mini-mal gradient encoding for robust estimation of diffusion anisot-ropy. Magn Reson Imaging, 18, 671–679.

  
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