Mean Kurtosis-Curve (MK-Curve) Correction Improves the Test-retest Reproducibility of Diffusion Kurtosis Imaging at 3 T

Diffusion kurtosis imaging (DKI) is applied to gain insights into the microstructural organization of brain tissues. However, the reproducibility of DKI outside brain white matter, particularly in combination with advanced estimation to remedy its noise sensitivity, remains poorly characterized. Therefore, in this study, we investigated the variability and reliability of DKI metrics while correcting implausible values with a fit method called mean kurtosis (MK)-Curve. A total of 10 volunteers (four women; age: 41.4 ± 9.6 years) were included and underwent two MRI examinations of the brain. The images were acquired on a clinical 3-T scanner and included a T1-weighted image and a diffusion sequence with multiple diffusion weightings suitable for DKI. Region of interest analysis of common kurtosis and tensor metrics derived with the MK-Curve DKI fit was performed, including intraclass correlation (ICC) and Bland-Altman (BA) plot statistics. A p value of less than 0.05 was considered statistically significant. The analyses showed good to excellent agreement of both kurtosis tensor- and diffusion tensor-derived MK-Curve-corrected metrics (ICC values: 0.77-0.98 and 0.87-0.98, respectively), with the exception of two DKI-derived metrics (axial kurtosis in the cortex: ICC = 0.68, and radial kurtosis in deep gray matter: ICC = 0.544). Non-MK-Curve-corrected kurtosis tensor-derived metrics ranged from 0.01 to 0.52 and diffusion tensor-derived metrics from 0.06 to 0.66, indicating poor to moderate reliability. No structural bias was observed in the BA plots for any of the diffusion metrics. In conclusion, MK-Curve-corrected DKI metrics of the human brain can be reliably acquired in white and gray matter at 3 T and DKI metrics have good to excellent agreement in a test-retest setting.