This review discusses the concept of diffusional kurtosis imaging (DKI), a technique that extends diffusion tensor imaging (DTI) to quantify non-Gaussian water diffusion in the brain. DKI is based on the kurtosis metric, which measures the deviation of the water diffusion probability distribution from a Gaussian distribution. The diffusional kurtosis is calculated using the fourth moment of the diffusion displacement distribution, and it provides additional information about the heterogeneity of the diffusion environment. DKI requires at least three b-values and 15 diffusion directions for accurate estimation. The review outlines the theoretical foundations of DKI, including the definitions of diffusion and kurtosis tensors, and discusses the practical considerations for data acquisition and post-processing. It also highlights the potential applications of DKI in studying ischemic stroke and neurodegenerative diseases such as Alzheimer's and schizophrenia. The review further explores the relationship between diffusional kurtosis and the diffusion-weighted NMR signal, and presents a series expansion approach for estimating the diffusional kurtosis from the NMR signal. The review also discusses the data acquisition protocols for DKI, including the choice of b-values and diffusion directions, and the post-processing steps for calculating the diffusion tensor and kurtosis tensor. The review concludes with a discussion of the potential applications of DKI in clinical settings and the challenges associated with its implementation.This review discusses the concept of diffusional kurtosis imaging (DKI), a technique that extends diffusion tensor imaging (DTI) to quantify non-Gaussian water diffusion in the brain. DKI is based on the kurtosis metric, which measures the deviation of the water diffusion probability distribution from a Gaussian distribution. The diffusional kurtosis is calculated using the fourth moment of the diffusion displacement distribution, and it provides additional information about the heterogeneity of the diffusion environment. DKI requires at least three b-values and 15 diffusion directions for accurate estimation. The review outlines the theoretical foundations of DKI, including the definitions of diffusion and kurtosis tensors, and discusses the practical considerations for data acquisition and post-processing. It also highlights the potential applications of DKI in studying ischemic stroke and neurodegenerative diseases such as Alzheimer's and schizophrenia. The review further explores the relationship between diffusional kurtosis and the diffusion-weighted NMR signal, and presents a series expansion approach for estimating the diffusional kurtosis from the NMR signal. The review also discusses the data acquisition protocols for DKI, including the choice of b-values and diffusion directions, and the post-processing steps for calculating the diffusion tensor and kurtosis tensor. The review concludes with a discussion of the potential applications of DKI in clinical settings and the challenges associated with its implementation.