A review of the automated detection of change in serial imaging studies of the brain

Abstract

Serial imaging is frequently performed on patients with diseases of the brain, to track and observe changes. Magnetic resonance imaging provides very detailed and rich information, and is therefore used frequently for this application. The data provided by MR can be so plentiful; however, that it obfuscates the information the radiologist seeks. A system which could reduce the large quantity of primitive data to a smaller and more informative subset of data, emphasizing change, would be useful. This article discusses motivating factors for the production of an automated process to this effect, and reviews the approaches of previous authors. The discussion is focused on brain tumors and multiple sclerosis, but many of the ideas are applicable to other disease processes, as well.

Figure 1

Possible steps in a practical change detection system.

Figure 2

The gray matter cluster underlies the white edema line of transition. Using a static method, such as classify-subtract, voxels moving along this transition would appear to change dramatically in gray matter membership. By focusing on direction of movement and using knowledge of the way transitions occur, rather than static feature space location at each time point, the problem of spurious gray matter change is obviated.

Figure 3

A sample change detection image. Type of change is encoded by color; magnitude of change is encoded by the intensity.