Abstract
BACKGROUND AND PURPOSE: Though CT is a highly calibrated imaging modality, head CT is typically interpreted qualitatively. Our aim was to initiate the establishment of a reference quantitative database for clinical head CT. MATERIALS AND METHODS: An automated segmentation algorithm was developed and applied to 354 clinical head CT scans with radiographically normal findings (ages, 18-101 years; 203 women) to measure brain volume, brain parenchymal fraction, brain radiodensity, and brain parenchymal radiomass. Brain parenchymal fraction was modeled using quantile regression analysis. RESULTS: Brain parenchymal fraction is highly correlated with age (R (2) = 0.908 for men and 0.950 for women), with 11% overall brain volume loss in the adult life span (1%/year from 20 to 50 years and 2%/year after 50 years of age). Third-order polynomial quantile regression curves for brain parenchymal fraction were rationalized and statistically validated. Total brain parenchymal radiodensity shows a decline as a function of age (14.9% for men, 14.7% for women; slopes not significantly different, P = .760). Age-related loss of brain radiomass (the product of volume and radiodensity) is approximately 20% for both sexes, significantly greater than the loss of brain volume (P < .001). CONCLUSIONS: An automated segmentation algorithm has been developed and applied to clinical head CT images to initiate the development of a reference database for quantitative brain CT imaging. Such a database can be subject to quantile regression analysis to stratify patient brain CT scans by metrics such as brain parenchymal fraction, radiodensity, and radiomass, to aid in the identification of statistical outliers and lend quantitative assessment to image interpretation.