Visualization and Post-processing of 5D Brain Images

Visualization plays a central role in the presentation and interpretation of medical image data. Radiologists and surgeons must be able to accurately interpret the data for diagnosis and surgical planning. The data obtained from many imaging systems can contain functional as well as structural information producing 4D datasets. In some cases this can extend to 5D when the image provides spectral information. Generally speaking, more information can be revealed in 5D than 4D imaging. Although several approaches are available to visualize 4D medical data, there is limited research on the visualization of 5D medical data. To present 5D medical datasets efficiently on a 2D screen provides considerable challenges to visualization. In this paper, a 5D brain EIT (electrical impedance tomography) dataset is used as a case study. The relationship and differences between multiple dimensional dataset visualization in different areas are analysed. A statistical post-processing method is then adopted to concentrate information included in the fifth dimension. A scheme to visualize 5D medical dataset is proposed and results are shown based on a simulated dataset