Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Efficient robust reconstruction of dynamic PET activity maps with radioisotope decay constraints
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
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Dynamic PET imaging provides important information for biological research, clinical diagnosis and pharmacokinetic analysis through kinetic modeling and data-driven parameter estimation. Kinetic parameters quantitatively describe dynamic material exchange and metabolism of radiotracers in plasma and tissues. While many efforts have been devoted to estimate kinetic parameters from dynamic PET, the poor statistical properties of the measurement data in low count dynamic acquisition and the uncertainties in estimating the arterial input function have limited the accuracy and reliability of the kinetic parameter estimation. Additionally, the quantitative analysis of individual kinetic parameters is not yet implemented. In this paper, we present a robust kinetic parameter estimation framework which is robust to both the poor statistical properties of measurement data in dynamic PET and the uncertainties in estimated arterial input function, and is able to analyze every single kinetic parameter quantitatively. The strategy is optimized with robust H∞ estimation under minimax criterion. Experiments are conducted on Monte Carlo simulated data set for quantitative analysis and validation, and on real patient scans for assessment of clinical potential.