Iterative Reconstruction Performance on the NeuroEXPLORER: Evaluation with Static and Dynamic Human Imaging

The NeuroEXPLORER (NX) is a next-generation, brain-dedicated PET scanner offering 10× higher sensitivity compared to the HRRT and < 2 mm spatial resolution. This work evaluated the impact of iterative reconstruction on the quantitative performance of NX using human ¹⁸F-SynVesT-1 and ¹¹C-PHNO data with an injection dose of 167.2 ± 13.6 MBq and 275.7 ± 64.8 MBq, respectively. Both 100% count level and 20% down-sampled count level were evaluated. Images were reconstructed with ordered-subsets expectation maximization incorporated PSF and TOF (PSF-TOF-OSEM) using 5 subsets and up to 20 iterations. Quantitative performance of static imaging was evaluated by computing bias and noise from statistically independent list-mode replicates. Simulations incorporating iteration-dependent bias and noise of each dynamic frame were used to evaluate effects on kinetic parameters. In static imaging, 5 subsets × 10 iterations achieved ROI bias < 10% and noise < 1% for ¹⁸F-SynVesT-1, and bias < 10% with noise < 10% for ¹¹C-PHNO. In dynamic imaging simulation, with 5 subsets and 15 iterations, kinetic parameter bias remained < 20% and noise < 5%. The results demonstrate quantitative stability and precision of NX across tracers and count levels.