This paper reviews the use of diffusional kurtosis imaging (DKI) to quantify non-Gaussianity in water diffusion in the brain. DKI is an extension of diffusion tensor imaging (DTI) that estimates the diffusional kurtosis, a measure of the degree of non-Gaussianity in water diffusion. The kurtosis is defined as the fourth moment of the probability distribution of water displacement, and it quantifies the heterogeneity of the diffusion environment. DKI is based on a series expansion of the diffusion-weighted signal intensity, which includes terms up to the second order in b-value (b). This expansion allows for the estimation of both the diffusion coefficient and the diffusional kurtosis using three or more b-values. The paper discusses the underlying theory, practical considerations for data acquisition and post-processing, and the relationship between DKI and other diffusion metrics such as mean diffusivity (MD) and fractional anisotropy (FA). It also explores various models for water diffusion, including multiple compartment models, a two-compartment model with water exchange, and a one-dimensional model with semi-permeable barriers. The authors argue that DKI is sensitive to diffusional heterogeneity and may be useful for investigating ischemic stroke and neuropathologies like Alzheimer's disease and schizophrenia. The paper provides detailed guidelines for data acquisition and post-processing, emphasizing the importance of choosing appropriate b-values and diffusion directions to ensure accurate parameter estimates.This paper reviews the use of diffusional kurtosis imaging (DKI) to quantify non-Gaussianity in water diffusion in the brain. DKI is an extension of diffusion tensor imaging (DTI) that estimates the diffusional kurtosis, a measure of the degree of non-Gaussianity in water diffusion. The kurtosis is defined as the fourth moment of the probability distribution of water displacement, and it quantifies the heterogeneity of the diffusion environment. DKI is based on a series expansion of the diffusion-weighted signal intensity, which includes terms up to the second order in b-value (b). This expansion allows for the estimation of both the diffusion coefficient and the diffusional kurtosis using three or more b-values. The paper discusses the underlying theory, practical considerations for data acquisition and post-processing, and the relationship between DKI and other diffusion metrics such as mean diffusivity (MD) and fractional anisotropy (FA). It also explores various models for water diffusion, including multiple compartment models, a two-compartment model with water exchange, and a one-dimensional model with semi-permeable barriers. The authors argue that DKI is sensitive to diffusional heterogeneity and may be useful for investigating ischemic stroke and neuropathologies like Alzheimer's disease and schizophrenia. The paper provides detailed guidelines for data acquisition and post-processing, emphasizing the importance of choosing appropriate b-values and diffusion directions to ensure accurate parameter estimates.