A method for assessing the significance of abnormalities in HMPO brain SPECT images.

Abstract

A normal atlas for HMPAO rCBF SPECT images was obtained from images of 53 normal controls. Following image registration and normalization, a mean image was extracted, while images representing correlated normal deviants were identified using principal component analysis. These images formed the "building blocks" of the atlas. For subsequent images, the atlas was used to create a "nearest normal equivalent" image, which was compared to a residual standard deviation image to determine the significance of deviations in the new image. Images from 30 patients (10 with Alzheimer's disease; 12 with single or multiple infarcts; and 8 normals) were analyzed. Using an optimal decision level, 10/10 patients with Alzheimer's disease and 11/12 patients with infarcts were correctly identified, with only one false-positive resulting. We utilized a database of images obtained from normal controls to create a normal atlas.