Diffusion MRI has showed great potential in probing tissue microstructure and brain structural connectivity. However, high-resolution diffusion MRI with multiple direction is limited by the lengthy scan time. In this abstract, we apply a kernel low rank model to accelerate diffusion imaging by undersampling the k-space. This method is validated using high-resolution mouse brain datasets. Compared with the conventional compressed sensing method, the proposed method demonstrate more accurate mean diffusivity, fractional anisotropy and fiber orientation distribution estimates with acceleration factors up to 8.