TY - JOUR

T1 - Potential of a statistical approach for the standardization of multicenter diffusion tensor data

T2 - A phantom study

AU - Timmermans, Charlotte

AU - Smeets, Dirk

AU - Verheyden, Jan

AU - Terzopoulos, Vasilis

AU - Anania, Vincenzo

AU - Parizel, Paul M.

AU - Maas, Andrew

N1 - © 2019 International Society for Magnetic Resonance in Medicine.

PY - 2019/4

Y1 - 2019/4

N2 - 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 x 10(-5)), "vendor" (P = 2.18 x 10(-5)), "head coil" (P = 9.00 x 10(-4)), "scanner drift," "bandwidth" (P = 0.033), "TE" (P = 8.20 x 10(-6)), "SNR" (P = 0.029) and "mean residuals" (P = 6.50 x 10(-4)) had a significant effect on the variability in mean FA. The variables "site" (P = 4.00 x 10(-4)), "head coil" (P = 2.00 x 10(-4)), "software" (P = 0.014), and "mean voxel outlier intensity count" (P = 1.10 x 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.

AB - 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 x 10(-5)), "vendor" (P = 2.18 x 10(-5)), "head coil" (P = 9.00 x 10(-4)), "scanner drift," "bandwidth" (P = 0.033), "TE" (P = 8.20 x 10(-6)), "SNR" (P = 0.029) and "mean residuals" (P = 6.50 x 10(-4)) had a significant effect on the variability in mean FA. The variables "site" (P = 4.00 x 10(-4)), "head coil" (P = 2.00 x 10(-4)), "software" (P = 0.014), and "mean voxel outlier intensity count" (P = 1.10 x 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.

KW - diffusion tensor imaging

KW - harmonization

KW - phantom

KW - variability

KW - statistical model

KW - linear mixed effects model

KW - FRACTIONAL ANISOTROPY

KW - IMAGING MEASUREMENTS

KW - MEAN DIFFUSIVITY

KW - BRAIN

KW - SCANNER

KW - VARIABILITY

KW - REPRODUCIBILITY

KW - ACQUISITION

KW - VOLUME

U2 - 10.1002/jmri.26333

DO - 10.1002/jmri.26333

M3 - Article

C2 - 30605253

VL - 49

SP - 955

EP - 965

JO - Journal of Magnetic Resonance Imaging

JF - Journal of Magnetic Resonance Imaging

SN - 1053-1807

IS - 4

ER -