Abstract

Dynamic images are temporal sequences of images, where the intensities of certain regions of interest (ROI's) change with time, whereas anatomical structures remain stationary. Here, new applications of dynamic image analysis, called similarity mapping, are reviewed. Similarity mapping identifies regions in a dynamic image sequence according to their temporal similarity or dissimilarity with respect to a reference ROI. Pixels in the resulting similarity map whose temporal sequence is similar to the reference ROI have high correlation values and are bright, while those with low correlation values are dark. Therefore, similarity mapping segments structures in a dynamic image sequence based on their temporal responses rather than spatial properties. The authors describe the abilities of similarity mapping to identify different image structures present in several dynamic MRI datasets with potential clinical value. They demonstrate that similarity mapping technique has been successful in identifying the following structures: 1) renal cortex and medulla, 2) activated areas of the brain during photic stimulation, 3) ischemia in the left coronary artery territory, 4) lung tumor, 5) tentorial meningioma, and 6) a region of focal ischemia in brain.