PT  - JOURNAL ARTICLE
AU  - Wang, Rongfu
AU  - Zhang, Sulei
AU  - Chen, Xueqi
AU  - Zhang, Jianhua
AU  - Chen, Lixin
AU  - Zhou, Yun
TI  - <strong>Estimating Input Function with Incomplete Blood Samples for Quantification of Nonhuman Primate Dynamic F-18-FDG PET/CT Using a Patlak Plot-based Optimization Method</strong>
DP  - 2020 May 01
TA  - Journal of Nuclear Medicine
PG  - 1406--1406
VI  - 61
IP  - supplement 1
4099  - http://jnm.snmjournals.org/content/61/supplement_1/1406.short
4100  - http://jnm.snmjournals.org/content/61/supplement_1/1406.full
SO  - J Nucl Med2020 May 01; 61
AB  - 1406Purpose: FDG uptake rate constant Ki is a main physiology parameter measured by dynamic PET study. A model-independent graphical analysis using Patlak plot with plasma input function is a standard approach used to estimate Ki. The plasma input function is the FDG time activity curve in plasma obtained by arterial blood sampling. The purpose of the study is to evaluate a Patlak plot-based optimization approach for noninvasive quantification of dynamic FDG PET. Methods: Eight 60-min monkey dynamic FDG PET studies with arterial blood samples were collected. The measured plasma input function (mPIF) was determined by arterial blood samples. Time activity curves of seven cerebral regions of interest were generated from each study. With a given number of blood samples, the estimated PIF (ePIF) was determined by either interpolation or extrapolation method using scale calibrated population mean of normalized PIF. The optimal time points for those blood samples to estimate PIF (ePIF) was determined by maximizing the correlations between the Ki estimated ePIF and ones estimated mPIF. A leave-two-out cross-validation method was used for evaluation. Results: The linear correlations between the Ki estimates from ePIF with optimal sampling schemes and those from measured PIF were: Ki (ePIF 1 sample) = 1.09 Ki (mPIF) - 0.00, R2 =0.95±0.08; Ki (ePIF 2 samples) = 1.09 Ki (mPIF) - 0.00, R2 =0.95±0.07; Ki (ePIF 3 samples) = 1.04 Ki (mPIF) - 0.00, R2 =0.96±0.05; and Ki (ePIF 4 samples) = 1.02 Ki (mPIF)-0.00, R2 =0.97±0.04. As sample size became greater or equal 4, the Ki estimates from ePIF with optimal protocol were almost identical to those from mPIF. Conclusions: The Patlak plot-based optimization approach is a robust method to estimate plasma input function for noninvasive quantification of non-human primate dynamic FDG PET. KEYWORDS FDG PET; Patlak plot; plasma input function; quantification