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

Na Zhang, Zhen-Sheng Deng, Fang Wang, Xiao-Yi Wang

Department of Radiology, XiangYa Hospital of School of Medicine, Central South University, Changsha, Hunan, P.R. China (410008);.
Institute of Biomedical Engineering, School of Info-physics and Geomatics Engineering, Central South University, Changsha, Hunan, P.R. China (410083).

DOI: 10.4236/jbise.2009.22018                

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.

Keywords

Diffusion Tensor Imaging, Diffusion Gradient, Signal Noise Ratio, Estimating Variance

Conflicts of Interest

The authors declare no conflicts of interest.

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.