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

Abstract

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 18F-MIZO and 18F-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 18F-MISO and/or 18F-FAZA PET/CT exams, reconstructed with OSEM and BPL. Lesion was considered as hypoxic if the lesion SUVmax > 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. SUVmax, SUVmean, 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 SUVmax 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

Determination of optimal regularization factor in Bayesian penalized likelihood reconstruction of brain PET images using [18F]FDG and [11C]PiB

“…The smoothing effect added further enhancement with increasing β values for the increased [ 11 C]PiB image noise caused by fewer counts due to the shorter half‐life of [ 11 C] than [ 18 F] 22 . Since the surrounding activity, as well as the desired count statistics and resolution differ among radiopharmaceutical tracers, 21,30 the β value of BPL should be optimized for [ 18 F]FDG and [ 11 C]PiB images 6,17,36 …”

Hypoxia in Lung Cancer Management: A Translational Approach

“…The specific features for each radiotracer are not discussed here. Among them, [ 18 F]-MISO ( 18 F-fluoromisonidazole) has been identified as a robust marker of hypoxia [ 159 , 160 ] with good reproducibility [ 161 ] and validity [ 162 , 163 , 164 ], highlighting [ 18 F]-MISO as a potential hypoxia-relevant biomarker, especially in radiation management [ 165 ]. [ 18 F]-HX4 PET imaging also seems promising to document hypoxia in NSCLC [ 166 , 167 , 168 ].…”

Clinical and phantom validation of a deep learning based denoising algorithm for F-18-FDG PET images from lower detection counting in comparison with the standard acquisition

“…This algorithm, incorporating a prior in the image distribution, allows the use of a high number of iterations, improving contrast while preventing any dramatic noise increase [ 1 , 2 ]. However, noise reduction is done at the expense of image quantitation [ 3 ].…”