A knowledge discovery approach to diagnosing myocardial perfusion

Abstract

Discusses applying a six-step discovery process to a database of SPECT bull's-eye maps of the heart. Visual assessment of clinical diagnostic images is observer-dependent. Thus, much effort is expended to computerize the process of diagnosis so it is less dependent on the observer, especially when the observer is not experienced. A large number of images to be evaluated (as in SPECT myocardial perfusion studies: approximately 15 oblique "slices," 15 oblique/sagittal, and 15 oblique/coronal, both in stress and rest, which comes to nearly 100 2-D images per patient) forced the creation of more "comprehensive" images; namely, the bull's-eye perfusion maps. Using these maps, the authors showed that it is possible to differentiate the patients with coronary artery disease (one- or two-vessel) from the patients with low probability of the disease (normals). In the future, features other than those used in this work will be used; for instance, a feature representing the area of "abnormal" myocardium, available in most previously mentioned algorithms for "normative" evaluation of bull's-eye maps. In the course of this work, the authors also came up with methods that can accurately extract the ROIs from an image where a thresholding method cannot be used.