Improved diffusion parameter estimation by incorporating T2 relaxation properties into the DKI-FWE model.

The free water elimination (FWE) model and its kurtosis variant (DKI-FWE) can separate tissue and free water signal contributions, thus providing tissue-specific diffusional information. However, a downside of these models is that the associated parameter estimation problem is ill-conditioned, necessitating the use of advanced estimation techniques that can potentially bias the parameter estimates. In this work, we propose the T2-DKI-FWE model that exploits the T2 relaxation properties of both compartments, thereby better conditioning the parameter estimation problem and providing, at the same time, an additional potential biomarker (the T2 of tissue). In our approach, the T2 of tissue is estimated as an unknown parameter, whereas the T2 of free water is assumed known a priori and fixed to a literature value (1573 ms). First, the error propagation of an erroneous assumption on the T2 of free water is studied. Next, the improved conditioning of T2-DKI-FWE compared to DKI-FWE is illustrated using the Cramér-Rao lower bound matrix. Finally, the performance of the T2-DKI-FWE model is compared to that of the DKI-FWE and T2-DKI models on both simulated and real datasets. The error due to a biased approximation of the T2 of free water was found to be relatively small in various diffusion metrics and for a broad range of erroneous assumptions on its underlying ground truth value. Compared to DKI-FWE, using the T2-DKI-FWE model is beneficial for the identifiability of the model parameters. Our results suggest that the T2-DKI-FWE model can achieve precise and accurate diffusion parameter estimates, through effective reduction of free water partial volume effects and by using a standard nonlinear least squares approach. In conclusion, incorporating T2 relaxation properties into the DKI-FWE model improves the conditioning of the model fitting, while only requiring an acquisition scheme with at least two different echo times.

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

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.