Scatter correction techniques in 3D PET: a Monte Carlo evaluation

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Abstract
In this work, a Monte Carlo software package, PET-EGS, designed to simulate realistic PET clinical studies, was used to assess three different approaches to scatter correction in 3D PET: analytical (gaussian fitting technique), experimental (dual energy window technique) and probabilistic (Monte Carlo technique). Phantom and clinical studies were carried out by 3D PET and simulated by PET-EGS. A clinical study (/sup 18/F-FDG brain study) was simulated assuming PET emission/transmission multiple-volume images as a voxelised source object describing the distribution of both the radioactivity and attenuation coefficients and accounting for out-of-field activity and media. The accuracy of PET-EGS in modelling the physical response of a 3D PET scanner was assessed by statistical comparison between measured and total (scatter+unscatter) simulated distributions (probability for the two distributions to be the same distribution: p>0.95). The accuracy of the scatter models, for each scatter correction technique, was evaluated on sinograms by statistical comparison between the estimated and the simulated scatter distributions (agreement <1 /spl sigma/). The accuracy of scatter correction was evaluated on sinograms by comparison between scatter corrected and simulated unscatter distributions, proving a comparable accuracy of all the considered scatter correction techniques for brainlike distributed sources.