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Quantitative brain (18)F-FDG PET studies often require the plasma time-activity curve (input function) for estimation of the cerebral metabolic rate of glucose (CMRglc). The gold standard for input function measurement is arterial blood sampling, which is invasive and time-consuming. Alternatively, input function can be estimated from dynamic images. This estimation often implies the use of manually placed regions of interest (ROIs) over cerebral vasculature, which is an operator-dependent and time-consuming task. The aim of our study was to compare 3 algorithms of image segmentation (local means analysis [LMA], soft-decision similar component analysis [SCA], and k-means) to automatically segment internal carotid arteries from dynamic (18)F-FDG brain studies.

the journal of nuclear medicine

Comparison of 3 Methods of Automated Internal Carotid Segmentation in Human Brain PET Studies: Application to the Estimation of Arterial Input Function