Attenuation and scatter correction of list-mode data driven iterative and analytic image reconstruction algorithms for rotating 3D PET systems

Image reconstruction directly from list-mode data requires different data correction techniques to standard projection-data based reconstruction, particularly in the case of iterative reconstruction. Attenuation and scatter correction techniques have been developed for two list-mode data driven reconstruction algorithms (FAIR-B (iterative) and Atrax (analytic/iterative)) recently proposed by the authors, and the results compared with two fast projection-data based algorithms (FORE+OSEM (iterative) and FORE+FBP (analytic)). List-mode data driven algorithms require event-by-event correction schemes, or alternatively image space procedures, as no direct operations to the completely sampled projection data set can be practically carried out. The methods developed in this work allow correction of list-mode data driven EM-ML type algorithms, such as FAIR, as well as analytic list-mode algorithms. The correction schemes have been applied to simulated data from various activity and attenuating medium distributions for a rotating 3D PET system. Both list-mode algorithms show some improvements in noise-contrast behaviour compared to the projection-data based methods.