Normalizing counts and cerebral blood flow intensity in functional imaging studies of the human brain

Abstract

Image intensity normalization is frequently applied to eliminate or adjust for subject or injection global blood flow (gCBF) and other sources of nuisance variation. Normalization has several other positive effects on the analysis of PET images. However, the choice of an intensity normalization technique affects the statistical and psychometric properties of the image data. We compared three normalization procedures, the ratio approach (regional (r)CBF/gCBF), histogram equalization, and ANCOVA, on both PET count and flow data sets. The ratio method presents the proportional increase of regions, the histogram equalization method offers the relative ranking of intensities over the image, and the ANCOVA method provides statistical deviations from an expected linear model of regional values from the subject's gCBF. The original study used 33 normal subjects in a standard subtraction paradigm. The normalization methods were evaluated on their ability to remove extraneous error variation, induce homogeneity of intersubject variation, and remove unwanted dependencies. In general, the normalization modified the subtraction image more than the individual condition images. All three methods worked well at removing the dependency of rCBF on gCBF in count and flow images. For count data, the three methods also reduced the amount of error variation equally well, improving the signal to noise ratio. For flow data, the histogram equalization and ratio methods worked best at reducing statistical error. All three methods dramatically stabilized the variance over the image.