Impact of the Bayesian penalized likelihood algorithm (Q.Clear®) in comparison with the OSEM reconstruction on low contrast PET hypoxic images

PURPOSE: To determine the impact of the Bayesian penalized likelihood (BPL) reconstruction algorithm in comparison to OSEM on hypoxia PET/CT images of NSCLC using (18)F-MIZO and (18)F-FAZA. MATERIALS AND METHODS: Images of low-contrasted (SBR = 3) micro-spheres of Jaszczak phantom were acquired. Twenty patients with lung neoplasia were included. Each patient benefitted from (18)F-MISO and/or (18)F-FAZA PET/CT exams, reconstructed with OSEM and BPL. Lesion was considered as hypoxic if the lesion SUV(max) > 1.4. A blind evaluation of lesion detectability and image quality was performed on a set of 78 randomized BPL and OSEM images by 10 nuclear physicians. SUV(max), SUV(mean,) and hypoxic volumes using 3 thresholding approaches were measured and compared for each reconstruction. RESULTS: The phantom and patient datasets showed a significant increase of quantitative parameters using BPL compared to OSEM but had no impact on detectability. The optimal beta parameter determined by the phantom analysis was β350. Regarding patient data, there was no clear trend of image quality improvement using BPL. There was no correlation between SUV(max) increase with BPL and either SUV or hypoxic volume from the initial OSEM reconstruction. Hypoxic volume obtained by a SUV > 1.4 thresholding was not impacted by the BPL reconstruction parameter. CONCLUSION: BPL allows a significant increase in quantitative parameters and contrast without significantly improving the lesion detectability or image quality. The variation in hypoxic volume by BPL depends on the method used but SUV > 1.4 thresholding seems to be the more robust method, not impacted by the reconstruction method (BPL or OSEM). TRIAL REGISTRATION: ClinicalTrials.gov, NCT02490696. Registered 1 June 2015