Quantitative Evaluation of PSMA PET Imaging using a Realistic Anthropomorphic Phantom and Shell-less Radioactive Epoxy Lesions

Abstract

Background Prostate specific membrane antigen (PSMA) PET images have shown superior performance in detecting metastatic prostate cancers. Relative to [18F]FDG PET images, PSMA PET images tend to visualize significantly higher-contrast focal lesions. We aim to evaluate segmentation and reconstruction algorithms in this emerging context. Specifically, Bayesian or maximum a posteriori (MAP) image reconstruction, compared to standard OSEM reconstruction, has received significant interest for its potential to reach convergence with minimal noise amplifications. However, few phantom studies have evaluated the quantitative accuracy of such reconstructions for high contrast, small lesions (sub-10mm) that are typically observed in PSMA images. In this study, we cast 3mm-16mm spheres using epoxy resin infused with a long half-life positron emitter (sodium-22; 22Na) to simulate prostate cancer metastasis. The anthropomorphic Probe-IQ phantom, which features a liver, bladder, lungs, and ureters, was used to model relevant anatomy. Dynamic PET acquisitions were acquired and images were reconstructed with OSEM (varying subsets and iterations) and BSREM (varying β parameters), and the effects on lesion quantitation were evaluated. Results The 22Na lesions were scanned against an aqueous solution containing fluorine-18 (18F) as the background. Due to the long half-life of 22Na compared to 18F, a separate scan with fully-decayed background was used to measure the ground truth radioactivity concentrations of the 22Na lesions. Regions-of-interest were drawn with MIM Software using 40% fixed threshold (40% FT) and a gradient segmentation algorithm (MIM’s PET Edge+). Recovery coefficients (RCs) (max, mean, peak, and newly defined “apex”) and metabolic tumour volume (MTV) were calculated for each sphere. SUVpeak and SUVapex had the most consistent RCs for different lesion-to-background ratios and reconstruction parameters. The gradient-based segmentation algorithm was more accurate than 40% FT for determining MTV, particularly for lesions \(\le\)6mm in diameter (R2 = 0.86–0.89 vs. R2 < 0.02, respectively). Conclusion An anthropomorphic phantom was used to evaluate quantitation for PSMA PET imaging of metastatic prostate cancer lesions. BSREM with β = 200–400 and OSEM with 1–2 iterations resulted in the most accurate SUV and MTV values for these imaging conditions. SUVapex, a hybrid metric of SUVmax and SUVpeak, was proposed for robust, accurate, and segmentation-free quantitation of lesions for PSMA PET.