Image-derived Input Functions for Cerebral Blood Flow Quantification from Dynamic 15O-water PET Images of Humans

Abstract Purpose Cerebral blood flow (CBF) quantification from dynamic $$^{15}$$ O-water positron emission tomography (PET) images via compartmental modeling requires measuring the tracer concentration in the arterial blood with respect to time. The existing methods for this include arterial blood sampling or extracting an image-derived input function (IDIF) from carotid arteries, neither of which is uncomplicated. However, new long axial field of view PET scanners have recently enabled the IDIF extraction from sites outside the head and neck area, such as the aorta, and new automatic segmentation tools allow an effortless volume of interest (VOI) definition. Here, our aim is to investigate the potential of the IDIFs from automatically generated aorta and common carotid artery (CCA) VOIs. Methods We compare the aorta and CCA IDIFs extracted from TotalSegmentator-defined VOIs with and without accounting for internal dispersion. We also introduce a new hybrid IDIF approach based on the use of CCA IDIF to adjust dispersion in the aorta IDIF. We systematically compute the CBF estimates with these five IDIFs from dynamic total-body $$^{15}$$ O-water PET images of 100 human patients. Results Compared to CCA IDIFs, the aorta IDIFs produced better model fit and less patient-wise variation, had no outlier values, and resulted in CBF estimates more consistent to earlier literature. Adjusting the dispersion significantly improved the model fit and affected the numerical values of the CBF estimates for both aorta and the CCA IDIFs. Conclusions The use of an IDIF extracted from an automatically generated aorta VOI in dynamic total-body $$^{15}$$ O-water PET images might offer an effective way of CBF quantification. More research is warranted for its validation. Based on our results, the use of automatically generated CCA VOIs is not recommended for this purpose.