Kinetic Parameter Sampling for Dynamic PET Imaging Using a Markov Chain Monte Carlo Method

Résumé fourni par la source

Dynamic positron emission tomography (PET) enables the quantification of glycolytic activity in human tissues and is widely utilized for the clinical diagnosis of cancer, heart disease, and neurological disorders.In this work, we develop a Markov Chain Monte Carlo (MCMC) method to sample the kinetic parameters, i.e., {𝐾 1 , 𝑘 2 , 𝑘 3 , 𝑘 4 , 𝑉 𝐵 }, in a voxel-wise manner based on a two-tissue compartment model using [ 18 F]FDG dynamic PET imaging.A Gaussian mixture model under a Bayesian framework is applied to distinguish different types of body tissue, where the types of body tissue are introduced as prior knowledge.The proposed method is assessed on both synthetic and clinical data and compared with a benchmark method, i.e., non-linear least squares.

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