Characterization of the image-derived carotid artery input function using independent component analysis for the quantitation of [18F] fluorodeoxyglucose positron emission tomography images

We previously developed a noninvasive technique for the quantification of fluorodeoxyglucose (FDG) positron emission tomography (PET) images using an image-derived input function obtained from a manually drawn carotid artery region. Here, we investigate the use of independent component analysis (ICA) for more objective identification of the carotid artery and surrounding tissue regions. Using FDG PET data from 22 subjects, ICA was applied to an easily defined cubical region including the carotid artery and neighboring tissue. Carotid artery and tissue time activity curves and three venous samples were used to generate spillover and partial volume-corrected input functions and to calculate the parametric images of the cerebral metabolic rate for glucose (CMRgl). Different from a blood-sampling-free ICA approach, the results from our ICA approach are numerically well matched to those based on the arterial blood sampled input function. In fact, the ICA-derived input functions and CMRgl measurements were not only highly correlated (correlation coefficients > 0.99) to, but also highly comparable (regression slopes between 0.92 and 1.09), with those generated using arterial blood sampling. Moreover, the reliability of the ICA-derived input function remained high despite variations in the location and size of the cubical region. The ICA procedure makes it possible to quantify FDG PET images in an objective and reproducible manner.