Assessment of population-based input functions for Patlak imaging of whole body dynamic 18F-FDG PET

Abstract

AbstractBackgroundArterial blood sampling is the gold standard method to obtain the arterial input function (AIF) for quantification of whole body (WB) dynamic18F-FDG PET imaging. However, this procedure is invasive and not typically available in clinical environments. As an alternative, we compared AIFs to population-based input functions (PBIFs) using two normalization methods: area under the curve (AUC) and extrapolated initial plasma concentration (CP*(0)). To scale the PBIFs, we tested two methods: (1) the AUC of the image-derived input function (IDIF) and (2) the estimatedCP*(0). The aim of this study was to validate IDIF and PBIF for FDG oncological WB PET studies by comparing to the gold standard arterial blood sampling.MethodsThe Feng18F-FDG plasma concentration model was applied to estimate AIF parameters (n= 23). AIF normalization used either AUC(0–60 min) orCP*(0), estimated from an exponential fit.CP*(0) is also described as the ratio of the injected dose (ID) to initial distribution volume (iDV).iDVwas modeled using the subject height and weight, with coefficients that were estimated in 23 subjects. In 12 oncological patients, we computed IDIF (from the aorta) and PBIFs with scaling by the AUC of the IDIF from 4 time windows (15–45, 30–60, 45–75, 60–90 min) (PBIFAUC) and estimatedCP*(0) (PBIFiDV). The IDIF and PBIFs were compared with the gold standard AIF, using AUC values and PatlakKivalues.ResultsThe IDIF underestimated the AIF at early times and overestimated it at later times. Thus, based on the AUC andKicomparison, 30–60 min was the most accurate time window for PBIFAUC; later time windows for scaling underestimatedKi(− 6 ± 8 to − 13 ± 9%). Correlations of AUC between AIF and IDIF, PBIFAUC(30–60), and PBIFiDVwere 0.91, 0.94, and 0.90, respectively. The bias ofKiwas − 9 ± 10%, − 1 ± 8%, and 3 ± 9%, respectively.ConclusionsBoth PBIF scaling methods provided good mean performance with moderate variation. Improved performance can be obtained by refining IDIF methods and by evaluating PBIFs with test-retest data.