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A prior information-based multi-population multi-objective optimization for estimating 18F-FDG PET/CT
Yiwei Xiong1, Siming Li1, Jianfeng He2,3
1Faculty of Information Engineering and Automation, Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China.
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 (K1, k2, k3, k4, fa, and [Formula: see text]) 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 K1 and k4 (P < 0.05 in the DeLong test) and the single population optimization in k2 and k3 (P < 0.05), and did not differ from other methods in fa and vb (P > 0.05). Compared with single-population optimization, the three p-MPMO methods improved the significant differences between K1, k2, k3, and k4. The p-MPMOPSO showed significant differences (P < 0.05) in the parameter estimation of k2, k3, k4, and fa. The p-MPMODE is implemented on K1, k2, k3, k4, and fa; 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.
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