Optimization of Q.Clear reconstruction for dynamic 18F PET imaging.

BACKGROUND: Q.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a ß penalization factor. This study aimed to determine the optimal ß-factor for accurate quantitation of dynamic PET scans. METHODS: A Flangeless Esser PET Phantom with eight hollow spheres (4-25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different ß-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CVRC) and root-mean-square error (RMSERC) were evaluated for the phantom data. Two male patients with recurrent glioblastoma were scanned on the same scanner using 18F-PSMA-1007. Using an irreversible two-tissue compartment model, the area under curve (AUC) and the net influx rate Ki were calculated to assess the impact of different ß-factors on the pharmacokinetic analysis of clinical PET brain data. RESULTS: In general, RC and CVRC decreased with increasing ß-factor in the phantom data. For small spheres (< 10 mm), and in particular for short acquisition times, low ß-factors resulted in high variability and an overestimation of measured activity. Increasing the ß-factor improves the variability, however at a cost of underestimating the measured activity. For the clinical data, AUC decreased and Ki increased with increased ß-factor; a change in ß-factor from 300 to 1000 resulted in a 25.5% increase in the Ki. CONCLUSION: In a complex dynamic dataset with variable acquisition times, the optimal ß-factor provides a balance between accuracy and precision. Based on our results, we suggest a ß-factor of 300-500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher ß-factors. TRIAL REGISTRATION: Clinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142 . EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO .