A prior information-based multi-population multi-objective optimization for estimating 18F-FDG PET/CT pharmacokinetics of hepatocellular carcinoma - Yiwei Xiong; Siming Li; Jianfeng He; Shaobo Wang (BMC Medical Imaging)
Abstract Background 18F fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) pharmacokinetics is an approach for efficiently quantifying perfusion and metabolic processes in the liver, but the conventional single-individual optimization algorithms and single-population optimization algorithms have difficulty obtaining reasonable physiological characteristics from estimated parameters. A prior-based multi-population multi-objective optimization (p-MPMOO) approach using two sub-populations based on two categories of prior information was preliminarily proposed for estimating the 18F-FDG PET/CT pharmacokinetics of patients with hepatocellular carcinoma. Methods PET data from 24 hepatocellular carcinoma (HCC) tumors of 5-min dynamic PET/CT supplemented with 1-min static PET at 60 min were prospectively collected. A reversible double-input three-compartment model and kinetic parameters (K 1, k 2, k 3, k 4, f a , and $$\:{v}_{b}$$ ) were used to quantify the metabolic information. The single-individual Levenberg–Marquardt (LM) algorithm, single-population algorithms (Particle Swarm Optimization (PSO), Differential Evolution (DE), and Genetic Algorithm (GA)) and p-MPMO optimization algorithms (p-MPMOPSO, p-MPMODE, and p-MPMOGA) were used to estimate the parameters. Results The areas under the curve (AUCs) of the three p-MPMO methods were significantly higher than other methods in K 1 and k 4 (P < 0.05 in the DeLong test) and the single population optimization in k 2 and k 3 (P < 0.05), and did not differ from other methods in f a and v b (P > 0.05). Compared with single-population optimization, the three p-MPMO methods improved the significant differences between K 1, k 2, k 3, and k 4. The p-MPMOPSO showed significant differences (P < 0.05) in the parameter estimation of k 2, k 3, k 4, and f a . The p-MPMODE is implemented on K 1, k 2, k 3, k 4, and f a ; The p-MPMOGA does it on all six parameters. Conclusions The p-MPMOO approach proposed in this paper performs well for distinguishing HCC tumors from normal liver tissue.