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

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Abstract

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 ≥ 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.

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.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (nos. 60963012, 61262034, 11272242, and 31271061), the Fundamental Research Funds for the Central Universities, the Key Project of Chinese Ministry of Education (no. 211087), the Natural Science Foundation of Jiangxi Province (no. 20132BAB201025), the Young Scientist Foundation of Jiangxi Province (no. 20122BCB23017), the Science and Technology Research Project of the Education Department of Jiangxi Province (no. GJJ13302), the Central Universities of Central South University (2013zzts251), the Doctoral Fund of Ministry of Education of China (20120201120071), and the Fundamental Research Funds for the Central Universities of China.

Conflict of interest

We declare that we have no conflict of interest.

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Authors and Affiliations

  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

    Xin Liu, Jin Xu & Pan Lin

  2. MRI Center, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, People’s Republic of China

    Jubao Sun

  3. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, People’s Republic of China

    Yong Yang

  4. School of Info-physics and Geomatics Engineering, Central South University, Changsha, People’s Republic of China

    Gang Yu

  5. Department of Medical Imaging, The First Affiliated Hospital of Medical College, Xi’an Jiaotong University, Xi’an, People’s Republic of China

    Chen Niu

  6. Nanjing Fullshare Superconducting Technology Company Limited, Nanjing, People’s Republic of China

    Hongjun Tian

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  1. Xin Liu
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  3. Jubao Sun
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  4. Gang Yu
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  5. Jin Xu
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Correspondence to Pan Lin.

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Xin Liu and Jubao Sun contributed equally to the manuscript.

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Table S1

Reproducibility of DTI measurements using the FLIRTreg registration algorithm. Note: highly reproducible cases (both intra- and intersession ICC ≥ 0.80 and CVws ≤ 10 %) are indicated in bold. Abbreviations: NDED, number of diffusion encoding directions; CC, corpus callosum; GCC, genu of the corpus callosum; SCC, splenium of the corpus callosum; PIC, posterior limb of the internal capsule; AIC, anterior limb of the internal capsule; ICC, intraclass correlation coefficient; CV, coefficient of variation; FA, fractional anisotropy, MD, mean diffusivity; λ∥, axial diffusivity; λ⊥, radial diffusivities. (DOC 93 kb)

Table S2

Reproducibility of DTI measurement using the FNIRTreg registration algorithm. Note: highly reproducible cases (both intra- and intersession ICC ≥ 0.80 and CVws ≤ 10 %) are indicated in bold. For abbreviations, see Table S1. (DOC 91 kb)

Table S3

Reproducibility of DTI measurement using the DWIreg registration algorithm. Note: highly reproducible cases (both intra- and intersession ICC ≥ 0.80 and CVws ≤ 10 %) are indicated in bold. For abbreviations, see Table S1. (DOC 91 kb)

Table S4

Reproducibility of DTI measurement using the DTITKreg registration algorithm. Note: highly reproducible cases (both intra- and intersession ICC ≥ 0.80 and CVws ≤ 10 %) are indicated in bold. For abbreviations, see Table S1. (DOC 91 kb)

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Liu, X., Yang, Y., Sun, J. et al. Reproducibility of diffusion tensor imaging in normal subjects: an evaluation of different gradient sampling schemes and registration algorithm. Neuroradiology 56, 497–510 (2014). https://doi.org/10.1007/s00234-014-1342-2

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