The immense progress of imaging technologies has radically changed the practice of medicine both in terms of diagnosis and intravascular surgery. Using technologies such as magnetic resonance imaging (MRI), and computed axial tomography (CAT) scans, doctors are now able to "see" internal organs and structures in high-resolution detail. Today using expensive specialized hardware, one can generate three-dimensional visualizations providing accurate interpretations and revolutionizing the medical field. This thesis presents a substantially different method to visualize volume datasets by treating them as a scattering volume and rendering the images on a small cluster of parallel computers. With sufficient computing power, the data can be explored interactively without any loss of information. We utilize a basic raycasting algorithm with several acceleration techniques, such as global empty space skipping, early ray termination, a local gradient cache and increased data access coherency. By selecting efficient data subdivisions, we eliminate the memory and bus-bandwidth latencies and maximize the computing power of each core. The cache coherence of the data access due to the bricking scheme produced almost real-time rendering speeds that are independent of the viewing direction. We tested these algorithms on three different datasets at varying output image resolutions. In the near future, with increased computing power and sufficient bandwidth, it will be possible to use a cluster of machines to render time-dependent datasets in real time and to deliver these images directly into an operating room.