Visualization of Multi-Modal and Time-Varying Medical Data Using Flow Visualization Approaches
In the medical field, time-varying data is a common occurrence. For example, patients could have a series of CT (computed tomography) or MRI (magnetic resonance imaging) scans taken over time to enable physicians to diagnose and follow progression of diseases such as multiple sclerosis or cancer. In addition, PET (Positron emission tomography) and SPECT (single photon emission computed tomography) imaging modalities are inherently time varying since human metabolism cannot be expected to remain unchanged from one moment to the next. Nonetheless, images from the medical domain are almost exclusively displayed as static pictures, and little attempt has been made to develop visual tools that emphasize changes over time. By contrast, visualization of fluid flow data is a well-studied problem. Various approaches have been developed to help scientists understand the motion of liquids and gasses. These include: Animations Glyphs - small arrow-like objects that point in the direction of the flow Streamlines and timelines - small (usually spherical) particles are placed in the flow field and followed over time Spot noise and texture splats - ellipse shaped spots are oriented in the direction of the flow Line integral convolution - spot noise is integrated along a streamline The objective of this study is to apply flow visualization methods to data in the medical domain, and to evaluate their effectiveness for various imaging modalities and applications. Following this, we hope to develop new techniques that will better suit the needs of the medical community, and that may have further-reaching applications outside of the medical domain.