Diffusion tensor imaging (DTI) can provide important macroscopic structural information for mouse brain but is limited by the available imaging resolution and inferentia tractography. To validate DTI tractography, it is paramount to merge DTI images and microscopic images with true representation of fibers. Recently, we have developed a micro-optical sectioning tomography (MOST) system which enables neurite level resolution. By using Advanced Normalization Tools, we have aligned our MOST dataset to the template dataset of DTI from Duke University. To optimize the computing efficacy, lower resolution of two different modal images has been used to get a displacement field of diffeomorphism. Then the displacement field is extended to the deformation of full resolution images. This has been a flexible strategy for 3D nonlinear mouse brain registration across different modalities and different resolutions. Under this kind of co-registration, we can show very fine neural fiber architectures as foreground (MOST) on the background (DTI) of the fibre density map. By comparing the derived maps between DTI and MOST, we have realized that DTI technique encounters pitfall for resolving complex neural fibres. Image fusion of mouse DTI and MOST dataset should promote both neural circuits study and DTI applications.