Evaluation of the spatial dependence of the point spread function in 2D PET image reconstruction using LOR-OSEM.

Abstract

The use of positron emission tomography (PET) imaging has proved beneficial in the staging and diagnosis of several cancer disease sites. Additional applications of PET imaging in treatment planning and the evaluation of treatment response are limited by the relatively low spatial resolution of PET images. Including point spread function (PSF) information in the system matrix (SM) of iterative reconstruction techniques has been shown to produce improved spatial resolution in PET images. In this study, the authors sampled the spatially variant PSF at over 6000 locations in the field of view for a General Electric Discovery ST PET/CT (General Electric Healthcare, Waukesha, WI) scanner in 2D acquisition mode. The authors developed PSF blurred SMs based on different combinations of the radial, depth, and azimuthal spatial dependencies to test the overall spatial dependence of the PSF on image quality. The PSF blurred SMs were included in a LOR-OSEM reconstruction algorithm and used for image reconstruction of geometric phantoms. The authors also examined the effect of sampling density on PSF characterization to design a more efficient sampling scheme. The authors found that depth dependent change in the amplitude of the detector response was the most important factor affecting image quality. A SM created from a PSF that introduced r (perpendicular to the LOR), d (parallel to the LOR), or r and d dependent blurring across the radial lines of response led to visually identifiable improvements in spatial resolution and contrast in reconstructed images compared to images reconstructed with a purely geometric SM with no PSF blurring. Images reconstructed using a SM with r and d dependent blurring across the radial lines of response showed improved spatial resolution and contrast-noise ratios compared to images reconstructed with a SM that had only r dependent blurring. Additionally, the authors determined that the PSF could be adequately characterized with roughly 85% fewer samples through the use of a better optimized sampling scheme. PET image reconstruction using a SM made from an accurately characterized PSF that accounts for r and d dependencies results in improved spatial resolution and contrast-noise relations, which may aid in lesion boundary detection for treatment planning or quantitative assessment of treatment response.