The target audience of this abstract is researchers investigating non-Gaussian water diffusion properties in biological tissues.Diffusion kurtosis imaging (DKI)¹ is a method used to quantify the non-Gaussian diffusion of water in biological tissues. It provides complementary information on white matter microstructure to that provided by the conventional diffusion tensor imaging (DTI) technique. However, in comparison to DTI, variability of DKI metrics is rarely studied. Previously variability was investigated for two DKI parameters^2,3, mean kurtosis (MK) and radial kurtosis (RK), in three major white matter structures, such as superior cingulum bundle (CG), medial motor corticospinal tract (CST), and mid-sagittal corpus callosum (CC). The aim of this work is to perform an investigation of single-subject between-session variability of all typical DTI/DKI parameters and its dependence on the anatomical region-of-interest. The data are to be compared with between-session variability of an anisotropic synthetic physical phantom additionally, and should also provide the baseline for a comparison with inter-subject variability.In vivo brain DKI data sets in healthy subjects (N=3) and a physical synthetic fibre phantom⁴ were acquired on a 3T Siemens MAGENETOM Tim Trio scanner using the following protocol: four b-values, b = 0, 700, 1000, 2500 s/mm²; 150 diffusion gradient directions; TE/TR=109ms/9000ms; voxel size = 2x2x2 mm³. The measurements were repeated in 10 different sessions for each subject and the phantom. Eddy current as well as motion distortions were corrected using the tool “EDDY”⁵. The corresponding rotation matrix was applied to the diffusion gradient directions. Bias due to background noise was corrected using the power-images method⁶. Gibbs ringing artefacts were corrected using the Total Variation method^{7, 8}. Finally, DKI parameters were evaluated using a weighted-linear least-squares approach available in the ExploreDTI toolbox. The diagonal elements of the kurtosis tensor were constrained to be positive. Variability was quantified using the coefficient of variation (CV), that represents the relative standard deviation. CV was estimated based on 10 session measurements for a total of 20 white matter tracts provided by the John Hopkins University (JHU) atlas⁹. The investigated WM structures comprised the left and right regions of 7 major association fibres, such as cingulum (gyrus) (Cg) and cingulum (hippocampus) (Ch), superior longitudinal fasciculus (SLF), SLF (temp), inferior longitudinal fasciculus (ILF), inferior fronto-occipital fasciculus (IFOF), uncinate fasciculus (UF), the left and right regions of 2 projection fibres, anterior thalamic radiation (ATR) and corticospinal tract (CST), and 2 commissural fibres, forceps major (F_major) and forceps minor (F_minor). Left and right regions of the same fibre will be denoted by subscripts “L” or “R”. Figures 1 and 2 show the CV values for DTI (Fig. 1) and DKI (Fig. 2) metrics for various fibres. One can see that CV strongly differs for various metrics and between various regions. Moreover, the CV values for DKI metrics tend to be larger than on DTI alone metrics, by approximately a factor of 1.5 – 2.0. The difference of CV for the DTI metrics for various fibres is especially high for UF, SLF (temp) and CH fibres. CV in FA metrics on ATR_L, SLF_L and ILF_R fibers is in the range between 0.015-0.025, different from other fibers where CV is below 0.008. MD range on UF_L and SLF (temp) fibers varies between 0.014-0.019, but for the rest of the fibers they are below 0.012. CV values of AD for CH, UF, and SLF (temp)_L is between 0.015-0.025, but for the rest of fibres they are below 0.015.CV values for RD for UF and SLF (temp) range is between 0.017-0.032, where as for the rest of fibers they are below 0.015. DKI shows significantly different behaviour from DTI. In particular, CV of KA is high for all fibres (0.015-0.048), but very low (0.01-0.02) on ATR, CST, CG, and CH_R. CV of MK stays in the same range between (0.07-0.034) that is similar to CV on all fibres for MD. CV for AK varies between (0.01-0.05) and for RK range for CV is 0.012-0.046, where the highest variability is shown on CH_L (0.032), F_minor (0.037) and SLF (temp)_L (0.047). We shall discuss our findings in the context of various reasons contributing to DTI/DKI parameter variability, in particular, those that are general for the method and those that are specific for in vivo brain.We demonstrate that all DTI/DKI parameters show heterogeneous single-subject, between-session variability in a number of anatomical regions. Our results provide the baseline for a comparison with inter-subject variability and should be useful for better interpretation of clinical studies subject to both intra- and inter-subject variability.