As 3D volumetric images of the human body become an increasingly crucial source of information for the diagnosis and treatment of a broad variety of medical conditions, advanced techniques that allow clinicians to efficiently and clearly visualize volumetric images become increasingly important. Interaction has proven to be a key concept in analysis of medical images because static images of 3D data are prone to artifacts and misunderstanding of depth. Furthermore, fading out clinically irrelevant aspects of the image while preserving contextual anatomical landmarks helps medical doctors to focus on important parts of the images without becoming disoriented. Therefore, we present techniques for multimodal volume rendering of medical data sets with a focus on visualization of diffusion tensor images. The techniques presented allow interactive filtering of information based of importance, directional information, and user-defined areas. By influencing the blending between the data sets, contextual information around the selected structures is preserved.