Whole-body PET parametric imaging employing direct 4D nested reconstruction and a generalized non-linear Patlak model

Nicolas A. Karakatsanis; Arman Rahmim

doi:10.1117/12.2043918

19 March 2014 Whole-body PET parametric imaging employing direct 4D nested reconstruction and a generalized non-linear Patlak model

Nicolas A. Karakatsanis, Arman Rahmim

Author Affiliations +

Proceedings Volume 9033, Medical Imaging 2014: Physics of Medical Imaging; 90330Y (2014) https://doi.org/10.1117/12.2043918
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Graphical analysis is employed in the research setting to provide quantitative estimation of PET tracer kinetics from dynamic images at a single bed. Recently, we proposed a multi-bed dynamic acquisition framework enabling clinically feasible whole-body parametric PET imaging by employing post-reconstruction parameter estimation. In addition, by incorporating linear Patlak modeling within the system matrix, we enabled direct 4D reconstruction in order to effectively circumvent noise amplification in dynamic whole-body imaging. However, direct 4D Patlak reconstruction exhibits a relatively slow convergence due to the presence of non-sparse spatial correlations in temporal kinetic analysis. In addition, the standard Patlak model does not account for reversible uptake, thus underestimating the influx rate Ki. We have developed a novel whole-body PET parametric reconstruction framework in the STIR platform, a widely employed open-source reconstruction toolkit, a) enabling accelerated convergence of direct 4D multi-bed reconstruction, by employing a nested algorithm to decouple the temporal parameter estimation from the spatial image update process, and b) enhancing the quantitative performance particularly in regions with reversible uptake, by pursuing a non-linear generalized Patlak 4D nested reconstruction algorithm. A set of published kinetic parameters and the XCAT phantom were employed for the simulation of dynamic multi-bed acquisitions. Quantitative analysis on the K_i images demonstrated considerable acceleration in the convergence of the nested 4D whole-body Patlak algorithm. In addition, our simulated and patient whole-body data in the postreconstruction domain indicated the quantitative benefits of our extended generalized Patlak 4D nested reconstruction for tumor diagnosis and treatment response monitoring.

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Nicolas A. Karakatsanis and Arman Rahmim "Whole-body PET parametric imaging employing direct 4D nested reconstruction and a generalized non-linear Patlak model", Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 90330Y (19 March 2014); https://doi.org/10.1117/12.2043918

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