Assessment of population-based input functions for the Patlak plot using whole body 18F-FDG PET imaging (2019)

519 Objectives: A whole body (WB) dynamic PET acquisition enables 18F-FDG parametric imaging of the WB. For routine use, an alternative to arterial blood sampling for estimating the input function (IF) is desired. WB PET scans typically cover large arterial blood regions such as the left ventricle and aorta which can be used to estimate image-derived IFs (IDIFs); however, the accuracy of IDIFs will be affected by body motion and partial volume effects. Furthermore, the injection must be performed with the patient on the bed in order to measure the early phase of the IDIF, further compromising clinically established workflow. An alternative is to use a population-based IF (PBIF), which is a normalized average of measured arterial blood data from several subjects. We evaluated and compared PBIFs normalized by two scaling methods to IDIFs and arterial data by assessing the Patlak Ki values.
 Methods: PET scans were acquired for 90 min on a 4 ring Biograph mCT PET/CT scanner and arterial blood sampling was performed on a subset of subjects (n = 13). 18F-FDG was injected using a 1 min infusion. A single bed cardiac PET scan was acquired for the first 6 min, followed by continuous bed motion dynamic scans (2 min x 4 passes, 5 min x 15 passes). An IDIF was automatically generated from the aorta region using the vendor’s ALPHA technology and the CT image [1]. A total of 23 independently measured arterial IF (AIF) datasets (11 healthy controls (HCs) and 12 patients) were used for generating the PBIF and 13 datasets (5 HCs and 8 cancer patients) were used for validation. To create the PBIFs, two normalization methods were applied: 1) PBIFAUC: using the area under the curve (AUC) of the AIF; and 2) PBIFAA/iDV: using the method of Vriens et al [2], which normalizes with the extrapolated initial plasma concentration (Cp[asterisk](0)) For studies with blood sampling, Cp[asterisk](0) is estimated using an exponential fit (Cp[asterisk](t)=Cp[asterisk](0)exp(-αt), 5 ≤ t ≤ 30 min). Cp[asterisk](0) can also be estimated from the ratio of 18F-FDG initial distribution volume (iDV) to administered activity (AA) (Cp[asterisk](0)=AA/iDV), and the iDV can in turn be estimated non-invasively using the subject weight and height (iDV=c(height)h(weight)w). The coefficients c, h, and w were estimated with the AIFs from the 23 subjects (c=1.08, h=0.65, w=0.48). The average curve of each normalized input function was computed. For PBIFAUC, 4 different time windows (15-45, 30-60, 45-75, and 60-90min) were tested to scale the PBIF using the AUC in each window. The AUC of the 6 estimated IFs (1 IDIF, 1 PBIFAA/iDV, 4 PBIFAUC) were compared to that of the AIF. Also, the Patlak Ki was determined for cerebellum, myocardium, and tumors (60-90 min) using each IF.
 Results: The R2 between the AUC(0-90min) values of the IDIF and those of the measured AIF was 0.87 (Table 1). The R2 was similar for the PBIF methods. In the Ki comparison, R2 using any PBIF method was higher than that using IDIF (Table 2). For PBIFAUC, among the 4 different time windows for scaling, 15-45 min provided the best AUC(0-90min). With the later time windows, the AUC of PBIFAUC was overestimated and Ki was underestimated. This was attributable to a slight bias in the IDIF at late times, i.e., SUV(15-30min) was similar between the AIF and IDIF, while SUV(60-90min) of IDIF was significantly larger than that of the AIF.
 Conclusions: Based on the regression slope and correlation coefficients, the best time-window to compute PBIFAUC was 15-45 min post-injection, which is inconvenient since the Patlak time window is 60-90min. PBIFAA/iDV and PBIFAUC(15-45min) produced similar regression lines and similar correlation coefficient values. Motion correction may improve the accuracy of PBIFAUC using late time windows for scaling. PBIFAA/iDV is not affected by motion and enables a simple scan protocol. Further evaluation with a larger dataset will be needed to define the optimal method.
Research Support: Siemens
References: [1] Tao et al, IEEE Trans Med Imaging, 30:338-350, 2011; [2] Vriens et al, J Nucl Med, 50:1933-1939, 2009.