NMR Biomed. 2022 Oct 26:e4856. doi: 10.1002/nbm.4856. Online ahead of print.
ABSTRACT
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 3T 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 intra-class correlation (ICC) and Bland-Altman (BA)plot statistics. A p-value <.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: in the range 0.77 – 0.98 and 0.87 – 0.98,resp.) with the exception of, two DKI derived metrics (Axial kurtosis in cortex: ICC=0.68, and radial kurtosis in deep grey matter: ICC =0.544). Non-MK-Curve corrected kurtosis tensor-derived metrics ranged between 0.01 – 0.52 and diffusion tensor-derived metrics between 0.06 – 0.66 indicating poor to moderate reliability. No structural bias was observed in the Bland-Altman 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 grey matter at 3T and DKI metrics have good to excellent agreement in a test-retest setting.
PMID:36285630 | DOI:10.1002/nbm.4856
