摘要
目的分析采用1.5T MR仪对正常成人臂丛神经进行DTI及纤维束示踪成像(DTT)的可行性及其量化特征。方法 34名健康志愿者接受DTI及DTT,测量C5~8双侧臂丛神经FA值、ADC值,采用单次激发自旋回波平面成像序列分别测量b值为700、9001、100 s/mm2时右侧C6神经根平均纤维束长度、纤维束所占体素及图像SNR。结果 34名健康志愿者中32名DTI及DTT成功。C5~8神经根平均FA值及ADC值依次为:0.46±0.03和(1.16±0.15)×10-3mm2/s、0.45±0.04和(1.13±0.19)×10-3mm2/s、0.44±0.04和(1.18±0.19)×10-3mm2/s、0.39±0.05和(1.26±0.18)×10-3mm2/s。b=900 s/mm2时,右侧C6神经根平均纤维长度、纤维束所占体素最大。b=700 s/mm2时,SNR最大(18.28±7.38);b=900 s/mm2时,SNR是最大SNR的93%。结论采用1.5T临床型MR机b值为900 s/mm2时,能成功完成正常臂丛神经DTI及DTT,清晰显示臂丛神经纤维束的FA值和结构。
Objective To analyze DTI and diffusion tensor tractography(DTT) parameters of normal adult brachial plexus,and to assess the feasibility and value of DTI and DTT for the brachial plexus with 1.5T MR system.Methods DTI and DTT of the brachial plexus were performed in 34 healthy adult volunteers.Maps of ADC and FA,as well as tractography of the brachial plexus were obtained.FA and ADC values of each root from C5 to C8 were measured.A single-shot spin-echo-based echo-planar imaging sequence was performed in each subject at 3 different b values including 700,900,and 1100 s/mm2.The length of reconstructed fiber tracts and fiber density index were calculated for the right C6 root.SNR was also calculated for each acquisition.Results Reconstructed DTI and DTT(32/34) were of good quality.FA and ADC values of each root from C5 to C8 were 0.46±0.03/(1.16±0.15)×10-3 mm2/s,0.45±0.04/(1.13±0.19)×10-3 mm2/s,0.44±0.04/(1.18±0.19)×10-3 mm2/s,0.39±0.05/(1.26±0.18)×10-3 mm2/s in turn.The longest fibers and maximum fiber density index were found at b values of 900 s/mm2.The maximum SNR(18.28±7.38) was found at b value of 700 s/mm2.SNR at b value of 900 s/mm2 was 93% of the maximum.Conclusion DTI and DTT can show the FA value and architecture of the brachial plexus.The optimal b value for DTI and fiber tractography of the brachial plexus at 1.5T was 900 s/mm2.
出处
《中国医学影像技术》
CSCD
北大核心
2012年第1期77-81,共5页
Chinese Journal of Medical Imaging Technology
基金
国家自然科学基金重点项目(81171800)
广东省自然科学基金(0630112)
关键词
臂丛
磁共振成像
扩散张量成像
Brachial plexus
Magnetic resonance imaging
Diffusion tensor imaging