Editorial for “Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T: Reproducibility and Quality of Fit”

Editorial for “Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T: Reproducibility and Quality of Fit” Diffusion tensor imaging (DTI) 1 is a well-established technique to probe in vivo tissue microstructure, especially in the white matter of the brain. Derived parameter maps from diffusion-weighted imaging (DWI) such as mean diffusivity (MD) and fractional anisotropy (FA) have become a staple contrast in the clinical workflow. Unavoidably, DTI has shortcomings. As the diffusion tensor model assumes Gaussian diffusion―or monoexponential signal decay―it cannot fit signals very well that show nonGaussian diffusion. This is, for example, the case in regions where white matter fibers cross, or in gray matter, where multiple compartments with different diffusion coefficients are present. Diffusion kurtosis imaging (DKI) may overcome these problems, because it can account for deviations from Gaussian diffusion, which is expressed as non-monoexponential signal decay in a voxel. When there is excess kurtosis along an axis (expressed as mean kurtosis, axial kurtosis, or radial kurtosis), it is likely that there are more barriers to diffusion because of the presence of cell membranes or myelin sheets in that voxel. DKI therefore provides a more accurate representation of the diffusion signal. DKI-derived parameters have been shown to be more sensitive to pathologies than DTI, for example, in cancer, Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, epilepsy, attention deficit hyperactivity disorder, traumatic brain injury, and cerebral infarction. As in all MRI techniques, better signal representation comes at a price: instead of one b-value to be acquired for DTI, a DKI acquisition needs multiple, and higher, b-values. Higher b-values lower the signal-to-noise ratio and increase the acquisition time. Of equal importance in clinical settings is image processing. Processing DKI data is not as straightforward as DTI. DTI processing is available directly on a clinical workstation, while DKI processing is not, and has to be performed with research software like DIPY. The final steps towards implementation of DKI in the clinic are 1) to assess reproducibility, and 2) robustness of the acquisition and processing methods towards heterogeneous patient populations and scanner types. The first step, test– retest reliability of high-resolution DKI measures, was assessed in the article by Kasa et al in this issue of JMRI. What is interesting about this well-executed study is that test–retest reliability was assessed in both 44 healthy volunteers from the Human Connectome Project, scanned in two separate sessions 5 3 months apart, and in 12 3D printed phantoms, scanned twice. The authors split the original datasets with three b-values (1000, 2000, 3000 s/mm) into subsets of (1000, 2000 s/mm) and (1000, 3000 s/mm) to investigate the influence of the number of b-values acquired on DKI reproducibility and fitting quality. The coefficient of variation was <2.8% in the phantoms, while in the white matter it was <10%. The quality of fitting, represented by the difference between the modeled and acquired diffusion data, was consistent across all subsets, indicating that good-quality DKI data can be obtained with two b-values. The most important finding in this study is that using only two b-values (1000, 3000 s/mm) gives similar reproducibility as when three b-values are used. For clinical studies, where scan time is limited and patient comfort is always an issue, this is a very welcome observation. The results from this study provide a good starting point for future studies in patients. Although it must be noted that the data from this study were collected with a modified scanner with high performance gradients on healthy volunteers. A next step, to really bring DKI to the clinic, is to assess the reproducibility on different clinical scanners and form a consensus on acquisition parameters and processing, as, for instance, was done successfully for arterial spin labeling.