Potential of a statistical approach for the standardization of multicenter diffusion tensor data: A phantom study. Issue 4 (3rd January 2019)

• Record Type: Journal Article
• Authors: Timmermans, Charlotte; Smeets, Dirk; Verheyden, Jan; Terzopoulos, Vasilis; Anania, Vincenzo; Parizel, Paul M.; Maas, Andrew
• Is Part Of: Journal of magnetic resonance imaging. Volume 49:Issue 4(2019)
• Page Start: 955
• Page End: 965
• Publication Date: 2019-01-03
• Subjects: diffusion tensor imaging -- harmonization -- phantom -- variability -- statistical model -- linear mixed effects model; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.26333
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : 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.