Magnetic resonance imaging is a medical imaging technique in which the phenomenon of nuclear magnetic resonance is used. This technique is widely used in clinical and scientific research at present. The diffusion of water molecules is isotropic in a homogeneous medium, while it is anisotropic in the structure of human tissue. Magnetic resonance diffusion tensor imaging (DTI) is for studying the microscopic structure inside body by using the water molecules’ diffusion effect which will reduce the signal intensity of magnetic resonance. Besides, it can quantitatively study the anisotropy of water molecules in three-dimensional space, and thus acquiring important pathological and physiological information without invading in vivo. In order to obtain the accurate result of the anisotropic diffusion of water molecules, according to a certain scheme, it is necessary to sequentially use diffusion sensitive gradient (DSG) magnetic fields in different diffusion orientations to measure the diffusion coefficient of water molecules for estimating the diffusion tensor. The precision of estimating diffusion tensor is affected by the applied DSG encoding scheme, and it is usually necessary to use a large number of linearly independent and evenly spatial distributions of DSG magnetic fields in order to make the tensor measurement result more accurate. Diffusion spectroscopy imaging technique and high angular resolution diffusion imaging (HARDI) technique are proposed for more complex fiber bundles crossing in human tissue, one of which, HARDI, has higher requirement for the number and the direction distribution uniformity of DSGs. In this paper, the basic principle of DTI and the DSG encoding schemes are reviewed, which includes completely random scheme, heuristic scheme, regular polyhedral scheme, numerically optimized scheme, etc. For the above various schemes their respective advantages and limitations are analyzed. At present, the Golden Ratio method is to be used in a new spherical DSG encoding scheme which meets the requirements for HARDI and can offer more accurate tensor estimation results in face of the corruption of data sets encountered in clinical practice.