Accelerate direct reconstruction of linear parametric images using nested algorithms

Conventional methods for generating parametric images in PET usually reconstruct a sequence of emission images from measured projection data first, and then fit the time activity curve (TAC) at each pixel to a linear or nonlinear kinetic model. To obtain an accurate estimate, the resolution and noise distribution of the reconstructed emission images should be modeled in the kinetic modeling. However, exact modeling of the noise distribution in emission images reconstructed by iterative methods is extremely difficult because the noise is space-variant and object-dependent. Often the space-varying noise variance and correlations between pixels are simply ignored in the kinetic modeling step, which leads to suboptimal results. Direct reconstruction of parametric images from raw projection data solves this problem by combining kinetic modeling and emission image reconstruction into a single formula. It allows accurate modeling noise statistics in data and hence is statistically more efficient [1], [2].