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The present paper proposes a novel computational method for parametricimaging of nuclear medicine data. The mathematical procedure is general enoughto work for compartmental models of diverse complexity and is effective in thedetermination of the parametric maps of all kinetic parameters governing tracerflow. We consider applications to [18F]-fluorodeoxyglucose Positron EmissionTomography (FDG-PET) data and analyze the two-compartment catenary modeldescribing the standard FDG metabolization by an homogeneous tissue, e.g. theliver, and the three-compartment non-catenary model representing the renalphysiology. The proposed imaging method starts from the reconstructed FDG-PETimages of tracer concentration and preliminarily applies image processingalgorithms for noise reduction and image segmentation processes for selectingthe region enclosing the organ of physiologic interest. The optimization schemesolves pixelwise the non-linear inverse problem of determining the kineticparameters from dynamic concentration data through a Gauss-Newton iterativealgorithm with a penalty term accounting for the ill-posedness of the problem.We tested our imaging approach on FDG-PET data of murine models obtained bymeans of a dedicated microPET system, and we analyzed different PET slicescontaining axial sections of the liver and axial sections of the kidneys. Thereconstructed parametric images proved to be reliable and qualitativelyeffective in the description of the local FDG metabolism with respect to thedifferent physiologies.

Parametric Imaging of FDG-PET Data Using Physiology and Iterative Regularization: Application to the Hepatic and Renal Systems