Multisphere phantom and analysis algorithm for PET image quality assessment

Abstract

PET image quality measurements of lesion detectability frequently use a small, radioactive sphere in a larger phantom. The typical analysis of a small single sphere in background has several shortcomings as a measure for detectability and quantitation: the measurement has low statistical power; the region of interest (ROI) is susceptible to large pixel-to-pixel fluctuations; only a single point in the axial and transaxial field of view is analyzed; background noise measurements in regions away from the signal sphere may bias the detectability measurement and user-placed ROIs can cause inconsistent measurements. For a more robust measurement and repeatable analysis of small lesion detectability in PET images, a multisphere phantom and analysis algorithm were developed. The multisphere phantom consists of a collection of 50 1.0-cm spheres, mounting rods and a gridded plate. A PET/CT study is presented where 29 spheres with a 4:1 sphere-to-background radioactivity ratio were acquired for multiple frame durations and reconstructed. An analysis algorithm was implemented and applied to the acquired PET/CT that detects the contrast-enhanced spheres in a CT, places ROIs on the spheres and their respective proximal background, applies the ROIs to the PET and performs quantitation. Results are presented that show the impact of increasing number of signal spheres and of different background ROI placement methods on the image quality measurement. Increasing the number of spheres reduced the variability in the image quality measurements, but only up to a point, beyond which increasing the number of spheres did not considerably reduce the variability. A phantom with numerous spherical inserts increases several measurement aspects: the flexibility of sphere placement during setup, the number of radioactivity concentrations that can be used during a single study and the statistical power of measurements. Additionally, an automated algorithm that localizes spheres, places ROIs and performs quantitation will increase reliability and reproducibility of image quality assessment, in addition to simplifying the analysis.