DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures) is a novel framework for inter-subject registration and template creation from Diffusion Tensor Imaging (DTI) data sets. This framework is optimized for brain data and its main goal is to achieve an accurate alignment of all brain structures, including white matter (WM), gray matter (GM), and spaces containing cerebrospinal fluid (CSF). The fundamental property of DR-TAMAS is to achieve global anatomical accuracy by incorporating in its cost function the most informative metrics locally. Another important feature of DR-TAMAS is a symmetric time-varying velocity-based transformation model, which enables it to account for potentially large anatomical variability in healthy subjects and patients.  In the DR-TAMAS frameowrk, the registration of diffusion tensor images can be complimented with anatomical images such as T1-weighted or T2-weighted structural images.

DR-TAMAS module provides several tools to perform rigid, affine and diffeomorphic registration of DTI data. It also contains tools to create atlases that are representative diffeomorphic averages of the population. It also provides tools to warp scalar, diffusion tensor and higher order models in addition to methods to perform tensor-based morphometry analysis.