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The advancements in medical imaging over the past decades have been remarkable and so is the relevance for today's medical procedures. The various imaging techniques have significantly improved both diagnosis and treatment. New insights have been gained and new therapy approaches have been developed. However, these advancements come at high costs. The required hardware and infrastructure are getting increasingly expensive. The enormous amount of data, generated by the scanners, needs to be stored and the data has to be processed, which is very time-consuming. To deal with these difficulties, high performance computer systems and sophisticated algorithms are used. A significant improvement can be achieved by the use of modern graphics hardware. The vector architecture of graphics processors, which combines numerous processing units into a single processor, is particularly suitable to process many data elements with the same processing directives. With respect to image processing this architecture is also very advantageous since mostly all data elements of an image or volume are processed in the same way which maps perfectly onto the graphics hardware. In combination with the for data streaming optimized memory interface of graphics hardware, the performance gain is considerable in comparison to a conventional implementation. However, the progress in algorithms in combination with specialized hardware causes software that is difficult to use. Along with the huge variety of different imaging modalities and the associated algorithms, this makes it difficult for the physician to work efficiently with the software. To counteract this development, among other things, the software developers have to improve the graphical user interface of the applications and optimize the workflows. During the work on this thesis, the software framework MedAlyVis (Medical Analysis and Visualization) was developed to support the physician in the work with image data. The workflow of processing and visualizing the image data is integrated into the application and modules for the different steps are provided. For performance improvements, most of the modules make use of programmable graphics hardware. In the course of preprocessing, filtering and registration are particularly time-consuming tasks and thus leave room for performance enhancements. In visualization, as the native application area of the hardware, the algorithms contribute to performance improvements as well as to quality enhancements. Yet, all the presented approaches are only of use, if they are applied to actual scenarios in medicine. Traditionally neurosurgery plays a leading role in the employment of new techniques. Due to the highly complicated problems and surgeries, neurosurgeons willingly adopt the new techniques for their activities. Consequently, the approaches presented in this thesis were first applied to diffusion tensor imaging and neurovascular compression which are two practical problems in neurosurgery.