A Study on Static Image Derived Input Function for Non-invasively Constructing Parametric Image in Functional Imaging

Positron emission tomography (PET), as functional imaging, provides in-vivo spatial distribution of physiological or biochemical processes. The kinetic modelling process to derive quantitative functional parameter usually requires invasive frequent blood sampling. We proposed a new approach to use static imaging derived information to produce non-invasive input function (SID-IF). The performance of SID-IF was investigated by 609 clinical neurological studies in non-invasively constructing parametric images of cerebral metabolic rate of glucose consumption (CMRGlc). The performance of the personal information feature based input function method (PIFB-IF) was also evaluated in the investigation. The results of area under curve and CMRGlc demonstrated the image feature derived by cerebellum provided less bias in the estimation of SID-IF. The performance of SID-IF was sensitive to the choice of the training set and the plasma glucose concentration in the data set may improve the estimated accuracy. The PIFB-IF method was less sensitive to the glucose range and choice of training set.