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

PurposeWe 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 [F-18]-FDG PET scans of the thigh. Our objective was to obtain nonbiased, low-variance K-m values without blood sampling.Materials and methodsWe 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.ResultsThree 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.ConclusionPreviously 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.