Skip to Main Content
Parameterization of a fast implementation of the Ollinger (1996) model-based 3-D scatter correction method for positron emission tomography (PET) has been evaluated using measured phantom data acquired on a GE Advance PET imaging system. The Ollinger method explicitly estimates the 3-D single-scatter distribution using measured emission and transmission data and then estimates the multiple-scatter as a convolution of the single scatter. The main algorithm difference from that implemented by Ollinger is that the scatter correction does not explicitly compute scatter for azimuthal angles; rather, it determines 2-D scatter estimates for data within 2-D "super-slices" using as input data from the 3-D direct-plane (nonoblique) slices. These axial super-slice data are composed of data within a parameterized distance from the center of the super-slice. A model-based scatter correction method can be parameterized, and choice parameters may significantly change the behavior of the algorithm. Parameters studied in this work included transaxial image downsampling, the number of detectors to calculate scatter to, multiples kernel width and magnitude, the number and thickness of super-slices, and the number of scatter estimation iterations. Measured phantom data included imaging of the NEMA NU-2001 image quality (IQ) phantom, the IQ phantom with 2 cm extra water-equivalent tissue strapped around its circumference, and an attenuation phantom (20 cm uniform cylinder with Teflon, water and air inserts) with two 8 cm diameter water-filled nonradioactive arms placed by its side. For the IQ phantom data, a subset of NEMA NU-2001 measures were used to determine the contrast-to-noise ratio (CNR), lung residual bias, and background variability. For the attenuation phantom, region of interests (ROIs) were drawn on the nonradioactive compartments and on the background. These ROIs were analyzed for inter and intra-slice variation, background bias, and compartment-to-background ratio. In most cases, the algorithm was most sensitive to multiple-scatter parameterization and least sensitive to transaxial downsampling. The algorithm showed convergence by the second iteration for the metrics used in this study. Also, the range of the magnitude of change in the metrics analyzed was - small over all changes in parameterization. Further work to extend these results to more realistic phantom and clinical datasets is warranted.