Potential of a statistical approach for the standardization of multicenter diffusion tensor data: A phantom study

• Published in: Journal of magnetic resonance imaging Vol. 49; no. 4; pp. 955 - 965
• Main Authors: Timmermans, Charlotte, Smeets, Dirk, Verheyden, Jan, Terzopoulos, Vasilis, Anania, Vincenzo, Parizel, Paul M., Maas, Andrew
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01-04-2019
• Subjects: Anisotropy; Biomarkers; Brain; Coefficient of variation; Computer programs; Diffusion; diffusion tensor imaging; Feasibility studies; Field strength; harmonization; Head; Image acquisition; Image quality; linear mixed effects model; Magnetic resonance imaging; Mathematical models; Neuroimaging; Outliers (statistics); Parameters; phantom; Software; Standard deviation; Standardization; Statistical analysis; Statistical methods; statistical model; Statistical models; Statistical tests; Structural integrity; Tensors; Upgrading; Variability; phantom; diffusion tensor imaging; linear mixed effects model; statistical model; variability; harmonization
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.26333

Background
Diffusion tensor imaging (DTI) parameters, such as fractional anisotropy (FA), allow examining the structural integrity of the brain. However, the true value of these parameters may be confounded by variability in MR hardware, acquisition parameters, and image quality.
Purpose
To examine the effects of confounding factors on FA and to evaluate the feasibility of statistical methods to model and reduce multicenter variability.
Study Type
Longitudinal multicenter study.
Phantom
DTI single strand phantom (HQ imaging).
Field Strength/Sequence
3T diffusion tensor imaging.
Assessments
Thirteen European imaging centers participated. DTI scans were acquired every 6 months and whenever maintenance or upgrades to the system were performed. A total of 64 scans were acquired in 2 years, obtained by three scanner vendors, using six individual head coils, and 12 software versions.
Statistical Tests
The variability in FA was assessed by the coefficients of variation (CoV). Several linear mixed effects models (LMEM) were developed and compared by means of the Akaike Information Criterion (AIC).
Results
The CoV was 2.22% for mean FA and 18.40% for standard deviation of FA. The variables “site” (P = 9.26 × 10−5), “vendor” (P = 2.18 × 10−5), “head coil” (P = 9.00 × 10−4), “scanner drift,” “bandwidth” (P = 0.033), “TE” (P = 8.20 × 10−6), “SNR” (P = 0.029) and “mean residuals” (P = 6.50 × 10−4) had a significant effect on the variability in mean FA. The variables “site” (P = 4.00 × 10−4), “head coil” (P = 2.00 × 10−4), “software” (P = 0.014), and “mean voxel outlier intensity count” (P = 1.10 × 10−4) had a significant effect on the variability in standard deviation of FA. The mean FA was best predicted by an LMEM that included “vendor” and the interaction term of “SNR” and “head coil” as model factors (AIC –347.98). In contrast, the standard deviation of FA was best predicted by an LMEM that included “vendor,” “bandwidth,” “TE,” and the interaction term between “SNR” and “head coil” (AIC –399.81).
Data Conclusion
Our findings suggest that perhaps statistical models seem promising to model the variability in quantitative DTI biomarkers for clinical routine and multicenter studies.
Level of Evidence: 4
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2019;49:955–965.