title: Reproducibility of diffusion tensor imaging in normal subjects: an evaluation of different gradient sampling schemes and registration algorithm.
abstract: BACKGROUND
  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 ≥ 0.70, (478/480) within-subject CV (CVws) ≤10.00 % 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: 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.
authors.forename: Xin
authors.initials: X
authors.lastname: Liu
authors.forename: Yong
authors.initials: Y
authors.lastname: Yang
authors.forename: Jubao
authors.initials: J
authors.lastname: Sun
authors.forename: Gang
authors.initials: G
authors.lastname: Yu
authors.forename: Jin
authors.initials: J
authors.lastname: Xu
authors.forename: Chen
authors.initials: C
authors.lastname: Niu
authors.forename: Hongjun
authors.initials: H
authors.lastname: Tian
authors.forename: Pan
authors.initials: P
authors.lastname: Lin
mesh_terms: D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D056324:Diffusion Tensor Imaging; D005260:Female; D006801:Humans; D008297:Male; D012016:Reference Values; D015203:Reproducibility of Results