Title: A physiology--based parametric imaging method for FDG--PET data,
Abstract: Parametric imaging is a compartmental approach that processes nuclear imaging
data to estimate the spatial distribution of the kinetic parameters governing
tracer flow. The present paper proposes a novel and efficient computational
method for parametric imaging which is potentially applicable to several
compartmental models of diverse complexity and which is effective in the
determination of the parametric maps of all kinetic coefficients. We consider
applications to [{18}F]-fluorodeoxyglucose Positron Emission Tomography
(FDG-PET) data and analyze the two-compartment catenary model describing the
standard FDG metabolization by an homogeneous tissue and the three-compartment
non-catenary model representing the renal physiology. We show uniqueness
theorems for both models. The proposed imaging method starts from the
reconstructed FDG-PET images of tracer concentration and preliminarily applies
image processing algorithms for noise reduction and image segmentation. The
optimization procedure solves pixelwise the non-linear inverse problem of
determining the kinetic parameters from dynamic concentration data through a
regularized Gauss-Newton iterative algorithm. The reliability of the method is
validated against synthetic data, for the two-compartment system, and
experimental real data of murine models, for the renal three-compartment
system.