Direct Estimation of Kinetic Parametric Images for Dynamic PET

Abstract

Dynamic positron emission tomography (PET) can monitor spatiotemporal distribution of radiotracer in vivo. The spatiotemporal information can be used to estimate parametric images of radiotracer kinetics that are of physiological and biochemical interests. Direct estimation of parametric images from raw projection data allows accurate noise modeling and has been shown to offer better image quality than conventional indirect methods, which reconstruct a sequence of PET images first and then perform tracer kinetic modeling pixel-by-pixel. Direct reconstruction of parametric images has gained increasing interests with the advances in computing hardware. Many direct reconstruction algorithms have been developed for different kinetic models. In this paper we review the recent progress in the development of direct reconstruction algorithms for parametric image estimation. Algorithms for linear and nonlinear kinetic models are described and their properties are discussed.

Development of 18F-labeled radiotracers for neuroreceptor imaging with positron emission tomography

“…each image point in the three-dimensional rectangular grid [263] . The higher the number of voxels, the higher the number of calculations to be executed.…”

“…juvenile pig [230] . Newer developments include proposals to obtain parametric images even in cases without either an arterial input function or a reference region [268] , direct reconstruction algorithms of linear and nonlinear parametric images, and joint estimation of parametric images and input function [263] . Further validation of these concepts is still needed.…”

“…For the two-tissue compartmental model, the slope of the regression line in the Logan plot represents the total distribution volume V T , defi ned by K 1 /k 2 (1+k 3 /k 4 ) + fbv (fbv = fractional blood volume in the target region, k 3 /k 4 = BP [267] ). Newer developments include proposals to obtain parametric images even in cases without either an arterial input function or a reference region [268] , direct reconstruction algorithms of linear and nonlinear parametric images, and joint estimation of parametric images and input function [263] . Further validation of these concepts is still needed.…”

Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors

“…For dynamic PET data, directly reconstruct the parametric images from the raw projection data can achieve improved accuracy and robustness [9,18,19]. The sparse image representation is directly applicable to dynamic PET data as it is a linear operation in the image domain.…”

“…When there is no regularisation, the ML problem can be solved by the expectation maximisation (EM) algorithm iteratively with a closed form update equation [8]. For some of the penalty models, the optimisation transfer technique can be applied to derive a closed form update from the surrogate functions of the original penalised likelihood function [9]. However such penalty models are usually not edge-preserving and would result in undesirable oversmooth on the edges and fine features in the image.…”