Rong Fu Wang
Peking University Health Science Center, China
Title: Generalization and optimization of a population-based input function estimation and approach for quantification of sparsely sampled dynamic 18F-FDG PET/CT data
Biography
Biography: Rong Fu Wang
Abstract
18F-FDG uptake rate constant Ki is a most interested and commonly used parameter for absolute quantification of 18F-FDG PET/CT. Ki is usually estimated by fitting a kinetic model with plasma input function (PIF) to the measured dynamic PET data. The need for arterial blood sampling to measure PIF (mPIF) is a main barrier to estimate Ki for clinical 18F-FDG PET. Two existing noninvasive PIF estimation methods, image derived PIF and simultaneous fitting method with kinetic model and parametric PIF, require image data to be acquired continuously and immediately post tracer injection. The objective of the study is to validate and optimize a generalized population-based PIF estimation method for noninvasive quantification of dynamic 18F-FDG PET for sparsely sampled PIF. Eight 60-min 27-frame monkey dynamic 18F-FDG PET studies were collected from a Philips Gemini GXL PET/CT with 3D data acquisition mode. Total 34 arterial blood samples were taken during PET scan as: 22 samples for the first 4 min, and followed by sampling at 5, 6, 8, 10, 12, 15, 20, 25, 30, 40, 50 and 60 min. Time activity curves (TACs) of 7 cerebral regions of interests (ROIs) were generated from each study. A generalized population-based approach to recover full kinetics of the PIF from sparsely sampled blood data is proposed. The estimated PIF (ePIF) from the incomplete PIF sampling data was determined by interpolation and extrapolation using scale-calibrated population mean of normalized PIFs. The optimal blood sampling scheme with given sample size m was determined by maximizing coefficient coefficients of Ki estimates between the Kis from measured PIF (mPIF) and estimated PIF (ePIF). The leave-two-out cross validation was performed. The linear correlations between the Ki estimates from the ePIF (with optimal sampling scheme) and those from the mPIF were: Ki(ePIF; 1 sample at 40 min) = 1.015Ki(mPIF) -0.000, R2 = 0.974; Ki(ePIF; 2 samples at 25 and 50 min) = 1.029Ki(mPIF) - 0.000, R2 = 0.985; Ki(ePIF; 3 samples at 8, 20, and 50 min) = 1.039Ki(mPIF) - 0.001, R2= 0.993; and Ki(ePIF; 4 samples at 8,12, 25, 40, and 55 min) = 1.02Ki(mPIF)-0.000, R2=0.997. The correlations of R2 from leave-2-out validation study were 0.978±0.007, 0.990±0.006, and 0.996±0.003 (mean ±SD) for 1, 2, and 3 samples of optimal sampling scheme, respectively. The generalized population-based PIF estimation method is a reliable method to estimate PIFs from incomplete blood sampling data for quantification of dynamic 18F-FDG PET using the Gjedde-Patlak plot.