Fast diffusion kurtosis imaging (DKI) with Inherent COrrelation-based Normalization (ICON) enhances automatic segmentation of heterogeneous diffusion MRI lesion in acute stroke.

Diffusion kurtosis imaging (DKI) has been shown to augment diffusion-weighted imaging (DWI) for the definition of irreversible ischemic injury. However, the complexity of cerebral structure/composition makes the kurtosis map heterogeneous, limiting the specificity of kurtosis hyperintensity to acute ischemia. We propose an Inherent COrrelation-based Normalization (ICON) analysis to suppress the intrinsic kurtosis heterogeneity for improved characterization of heterogeneous ischemic tissue injury. Fast DKI and relaxation measurements were performed on normal (n = 10) and stroke rats following middle cerebral artery occlusion (MCAO) (n = 20). We evaluated the correlations between mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) derived from the fast DKI sequence and relaxation rates R1 and R2 , and found a highly significant correlation between MK and R1 (p < 0.001). We showed that ICON analysis suppressed the intrinsic kurtosis heterogeneity in normal cerebral tissue, enabling automated tissue segmentation in an animal stroke model. We found significantly different kurtosis and diffusivity lesion volumes: 147 ± 59 and 180 ± 66 mm3 , respectively (p = 0.003, paired t-test). The ratio of kurtosis to diffusivity lesion volume was 84% ± 19% (p < 0.001, one-sample t-test). We found that relaxation-normalized MK (RNMK), but not MD, values were significantly different between kurtosis and diffusivity lesions (p < 0.001, analysis of variance). Our study showed that fast DKI with ICON analysis provides a promising means of demarcation of heterogeneous DWI stroke lesions.