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http://dx.doi.org/10.3348/kjr.2015.16.2.410

Effect of Increasing Diffusion Gradient Direction Number on Diffusion Tensor Imaging Fiber Tracking in the Human Brain  

Yao, Xufeng (School of Optical-Electrical and Computer Engineering, Shanghai Medical Instrument College, University of Shanghai for Science and Technology)
Yu, Tonggang (Department of Radiology, Huashan Hospital, Fudan University)
Liang, Beibei (School of Optical-Electrical and Computer Engineering, Shanghai Medical Instrument College, University of Shanghai for Science and Technology)
Xia, Tian (School of Optical-Electrical and Computer Engineering, Shanghai Medical Instrument College, University of Shanghai for Science and Technology)
Huang, Qinming (School of Optical-Electrical and Computer Engineering, Shanghai Medical Instrument College, University of Shanghai for Science and Technology)
Zhuang, Songlin (School of Optical-Electrical and Computer Engineering, Shanghai Medical Instrument College, University of Shanghai for Science and Technology)
Publication Information
Korean Journal of Radiology / v.16, no.2, 2015 , pp. 410-418 More about this Journal
Abstract
Objective: To assess the effects of varying the number of diffusion gradient directions (NDGDs) on diffusion tensor fiber tracking (FT) in human brain white matter using tract characteristics. Materials and Methods: Twelve normal volunteers underwent diffusion tensor imaging (DTI) scanning with NDGDs of 6, 11, 15, 21, and 31 orientations. Three fiber tract groups, including the splenium of the corpus callosum (CC), the entire CC, and the full brain tract, were reconstructed by deterministic DTI-FT. Tract architecture was first qualitatively evaluated by visual observation. Six quantitative tract characteristics, including the number of fibers (NF), average length (AL), fractional anisotropy (FA), relative anisotropy (RA), mean diffusivity (MD), and volume ratio (VR) were measured for the splenium of the CC at the tract branch level, for the entire CC at tract level, and for the full brain tract at the whole brain level. Visual results and those of NF, AL, FA, RA, MD, and VR were compared among the five different NDGDs. Results: The DTI-FT with NDGD of 11, 15, 21, and 31 orientations gave better tracking results compared with NDGD of 6 after the visual evaluation. NF, FA, RA, MD, and VR values with NDGD of six were significantly greater (smallest p = 0.001 to largest p = 0.042) than those with four other NDGDs (11, 15, 21, or 31 orientations), whereas AL measured with NDGD of six was significantly smaller (smallest p = 0.001 to largest p = 0.041) than with four other NDGDs (11, 15, 21, or 31 orientations). No significant differences were observed in the results among the four NDGD groups of 11, 15, 21, and 31 directions (smallest p = 0.059 to largest p = 1.000). Conclusion: The main fiber tracts were detected with NDGD of six orientations; however, the use of larger NDGD (${\geq}11$ orientations) could provide improved tract characteristics at the expense of longer scanning time.
Keywords
Diffusion tensor imaging; Number of diffusion gradient directions; Fiber tracking; Tract characteristics;
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