Visualization and Exploration of Time-varying and Diffusion Tensor Medical Image Data Sets

In this work, we propose and compare several methods for the visualization and exploration of time-varying volumetric medical images based on the temporal characteristics of the data. The principle idea is to consider a time-varying data set as a 3D volume where each voxel contains a time-activity curve (TAC). We define and appraise three different TAC similarity measures. Based on these measures we introduce three methods to analyze and visualize time-varying data. The first method relates the whole data set to one template TAC and creates a 1D histogram. The second method extends the 1D histogram into a 2D histogram by taking the Euclidean distance between voxels into account. The third method does not rely on a template TAC but rather creates a 2D scatter plot of all TAC data points via multi-dimensional scaling. These methods allow the user to specify transfer functions on the 1D and 2D histograms and on the scatter plot, respectively. We validate these methods on synthetic dynamic Single Photon Emission Computed Tomography and Positron Emission Tomography data sets and a dynamic planar Gamma camera image of a patient. We use a similar idea to visualize diffusion tensor imaging. We will illustrate this visualization approach on a real patient data set. These techniques are designed to offer researchers and health care professionals a new tool to study time-varying and diffusion tensor medical imaging data sets.