The tensor data of subjects are usually averaged linearly over multiple channels to obtain the tensor template. However, linear averaging ignores the vector information in the tensor. Additionally, it will render the interface between the gray matter and white matter too smooth, resulting in resolution reduction. To address the above problems, this paper introduced quaternion and Gaussian weighted average to construct a Gaussian diffusion tensor imaging (DTI) brain template. First, the DTI data of 55 healthy subjects were preprocessed to minimize data artifacts. The obtained data were then subjected to preliminary spatial standardization. Then, the tensor was decomposed to acquire eigenvectors and eigenvalues. Finally, the eigenvalues and quaternion converted from the eigenvectors were followed by Gaussian weighted average to gain the averaged eigenvectors and eigenvalues. The tensor template was obtained by reconstructing the averaged eigenvectors and eigenvalues. The experimental results show that compared with the linear DTI template, the Gaussian DTI template performs better on the DTED, COH, DVED, OVL, and corr_FA evaluation indicators but poorer on the IA indicator. The Gaussian DTI template proposed in this paper has certain advantage on the overall information retention, but is to be further improved on the orientation information.