Reproducibility of diffusion tensor imaging in normal subjects: an evaluation of different gradient sampling schemes and registration algorithm

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• 作者：Xin Liu (1)
  Yong Yang (3)
  Jubao Sun (2)
  Gang Yu (4)
  Jin Xu (1)
  Chen Niu (5)
  Hongjun Tian (6)
  Pan Lin (1)
  
• 关键词：Diffusion tensor imaging ; Reproducibility ; Diffusion ; encoding directions ; Registration
• 刊名：Neuroradiology
• 出版年：2014
• 出版时间：June 2014
• 年：2014
• 卷：56
• 期：6
• 页码：497-510
• 全文大小：3,708 KB
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• 作者单位：Xin Liu (1)
  Yong Yang (3)
  Jubao Sun (2)
  Gang Yu (4)
  Jin Xu (1)
  Chen Niu (5)
  Hongjun Tian (6)
  Pan Lin (1)
  
  1. Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi’an Jiaotong University, No. 28, Xianning West Road, Xi’an, Shaanxi, 710049, People’s Republic of China
  3. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, People’s Republic of China
  2. MRI Center, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, People’s Republic of China
  4. School of Info-physics and Geomatics Engineering, Central South University, Changsha, People’s Republic of China
  5. Department of Medical Imaging, The First Affiliated Hospital of Medical College, Xi’an Jiaotong University, Xi’an, People’s Republic of China
  6. Nanjing Fullshare Superconducting Technology Company Limited, Nanjing, People’s Republic of China
  
• ISSN：1432-1920

文摘

Introduction Diffusion tensor imaging (DTI) is very useful for investigating white matter integrity in ageing and neurological disorders; thus, evaluating its reproducibility under different acquisition protocols and analysis methods may assist in the design of clinical studies. Methods To measure the reproducibility of DTI in normal subjects, this study include (1) depicting the reproducibility of DTI measurements in commonly used regions-of-interest analysis by intraclass correlation coefficient (ICC) and coefficient of variation (CV), (2) evaluating and comparing inter and intrasession test-retest reproducibility, and (3) illustrating the effect of the number of diffusion-encoding directions (NDED) and registration algorithms on measurement reproducibility. Results DTI measurements exhibit high reproducibility, with overall (430/480) ICC?≥-.70, (478/480) within-subject CV (CVws) ?0.00?% and between-subject CV (CVbs) ranging from 1.32 to 13.63?%. Repeated measures ANOVAs and paired t tests were conducted to compare inter and intrasession reproducibility with different diffusion sampling schemes and registration algorithms. Our results also confirmed that increasing the NDED could improve the accuracy and reproducibility of DTI measurements. In addition, we compared reproducibility indices that were derived using different registration algorithms, and a tensor-based deformable registration yielded the most reproducible results. Finally, we found that increasing the NDED could reduce the difference between the reproducibility of measurement derived using different registration algorithms and between the reproducibility of intersession and intrasession. Conclusion Our results suggest that the choice of DTI acquisition protocol and post-processing methods can influence the accurate estimation and reproducibility of DTI measurements and should be considered carefully for clinical applications.