Evaluation of different reconstruction algorithms in contrast recovery for noncentrally located small lesions

1775 Objectives: It has been shown that recent advances in PET iterative reconstruction such as Bayesian penalized likelihood (BPL), point spread function (PSF) modeling, and time-of-flight (TOF) improve image quality and quantitative accuracy. The phantom based evaluations were performed mainly for spheres located near the center of the field of view (FOV). Our goal is to evaluate six different reconstruction algorithms in the precise estimation of contrast recovery (CR) for small lesions at various axial and transaxial locations. Methods: A NEMA image quality phantom was filled with 18F for a simulation of tumor imaging with 5 hot spheres and 1 cold sphere in mild background activity at ~ 2:1 activity ratio. The largest sphere with the 37mm diameter was nonradioactive. A PET/CT scanner using SiPM and LYSO crystals was used for this study. We scanned the phantom at 5 axial and 3 transaxial planes, totaling 15 different locations within a 20-cm PET FOV. The scan duration was 4 min for the first data and was increased for the subsequent scans to compensate for the decay. We then reconstructed each data using six different algorithms: OSEM (VPHD), OSEM with PSF modeling (VPHD+S), OSEM with BPL (VPHD+QC), TOF OSEM (VPFX), TOF with PSF (VPFX+S), and TOF with BPL (VPFX+QC). Filters and iterations for each algorithm were chosen to achieve an equal noise level [Table]. Coefficient of variation (CV) was used as a measure of noise in the phantom background when the center of the spheres was at the center of the PET FOV. We reviewed the CV of the normal liver uptakes in clinical FDG PET exams performed on the same scanner and found the average CV is ~9% when the PET images were reconstructed using our routine protocol using VPFX. For a sphere in each of the 90 PET images, a circular region of interest (ROI) on the central slice of sphere and 10 background ROIs with the same size were drawn. The CR was calculated using the ratio of the average counts within the sphere ROI and the background ROIs, and the actual activity ratio in the phantom. The CR of each sphere was averaged over 15 locations and the standard deviation of the mean was calculated. To obtain the percentage variability of the CR for each sphere, we divided the standard deviation by the mean and multiply by 100. Results: Each reconstruction demonstrated a similar pattern of contrast recovery with least amount of contrast and most variability with smaller sized spheres and more contrast recovery and least variability for larger sized spheres [Figure]. For the 17mm diameter sphere, the CR variability was 15.5% (VPHD), 13.9% (VPHD+S), 13.0% (VPHD+QC), 17.3% (VPFX), 15.6% (VPFX+S), and 12.0% (VPFX+QC). For the 28mm sphere, it was 9.8% (VPHD), 10.4% (VPHD+S), 10.0% (VPHD+QC), 7.2% (VPFX), 7.2% (VPFX+S), and 6.9% (VPFX+QC). All the VPFX algorithms resulted in a more precise estimation of tumor SUV compared to corresponding VPHD algorithm, specifically for larger spheres. Also, the BPL algorithm was the most precise in the CR measurement for both the VPHD and the VPFX sets. Conclusions: We have shown that the variability of CR within the imaging FOV depends on the sphere size and reconstruction methods. The BPL reconstruction allows for the most precise contrast recovery that stays consistent regardless to position within the PET FOV. Since tumors can be imaged at any locations within the PET FOV, this study will be useful in understanding the precision of the measured SUV in tumor imaging.