Correlation of NEC to Image SNR and Lesion Detectability in Clinical PET

In positron emission tomography (PET) imaging, Noise Equivalent Counts (NEC) serve as a key indicator of overall system performance. Previous studies have established the relationship between NEC and signal-to-noise (SNR) for both non-time-of-flight (non-TOF) and TOF PET scanners employing filtered back-projection (FBP) algorithms. This study aims to explore the correlation between NEC and image SNR, as well as task-specific metrics, using iterative reconstruction with comprehensive TOF PET system modeling. The phantom, 35 cm in diameter to represent a patient with $\mathrm{BMI}\gt30$, featured 12 1-cm diameter spheres placed in an off-centered slice, and data were acquired across a wide range of activity levels using the PennPET Explorer. Multiple scans were generated via bootstrapping and reconstructed using list-mode TOF-ordered subset expectation maximization (TOF-OSEM) algorithms to obtain mean and standard deviation images. Image SNR was quantified as the ratio of mean values within a central volume-of-interest (VOI) across both images. Utilizing a generalized scan-statistic model, the area under the localization receiver operating characteristic (ALROC) was calculated for the spheres placed at the offset slice. NEC was computed using both conventional and TOF-adapted formulas from the original data. The results indicate that despite the linearity between SNR and NEC metrics, neither conventional NEC nor $\mathrm{NEC}_{\text {TOF }}$ metrics accurately represent the global image SNR when employing iterative reconstruction methods. Furthermore, our results show that although SNR is proportional to ALROC, it does not adequately represent the change in ALROC as TOF resolution improves.