Calibrated image-derived input functions for the determination of the metabolic uptake rate of glucose with [(18)F]-FDG PET

PURPOSE: We investigated the use of a simple calibration method to remove bias in previously proposed approaches to image-derived input functions (IDIFs) when used to calculate the metabolic uptake rate of glucose (K(m)) from dynamic [(18)F]-FDG PET scans of the thigh. Our objective was to obtain nonbiased, low-variance K(m) values without blood sampling. MATERIALS AND METHODS: We evaluated eight previously proposed IDIF methods. K(m) values derived from these IDIFs were compared with K(m) values calculated from the arterial blood samples (gold standard). We used linear regression to extract calibration parameters to remove bias. Following calibration, cross-validation and bootstrapping were used to estimate the mean square error and variance. RESULTS: Three of the previously proposed methods failed mainly because of zero-crossings of the IDIF. The remaining five methods were improved by calibration, yielding unbiased K(m) values. The method with the lowest SD yielded an SD of 0.0017/min – that is, below 10% of the muscle K(m) value in this study. CONCLUSION: Previously proposed IDIF methods can be improved by using a simple calibration procedure. The calibration procedure may be used in other studies, thus obviating the need for arterial blood sampling, once the calibration parameters have been established in a subgroup of participants. The method has potential for use in other parts of the body as it is robust with regard to partial volume effects.