The issue of reconstruction dependency in SUV values that hampers the comparison of data between different PET systems can be overcome using two reconstructions for harmonized quantification and optimal diagnosis or using the EQ.PET technology. Both technologies produce similar results, EQ.PET sparing reconstruction and interpretation time. Other manufacturers are encouraged to either emulate this solution or to produce a vendor-neutral approach.

BackgroundThe aim of this study is to determine if the choice of the 18F-FDG-PET protocol, especially matrix size and reconstruction algorithm, is of importance to discriminate between immunohistochemical subtypes (luminal versus non-luminal) in breast cancer with textural features (TFs).ProceduresForty-seven patients referred for breast cancer staging in the framework of a prospective study were reviewed as part of an ancillary study. In addition to standard PET imaging (PSFWholeBody), a high-resolution breast acquisition was performed and reconstructed with OSEM and PSF (OSEMbreast/PSFbreast). PET standard metrics and TFs were extracted. For each reconstruction protocol, a prediction model for tumour classification was built using a random forests method. Spearman coefficients were used to seek correlation between PET metrics.ResultsPSFWholeBody showed lower numbers of voxels within VOIs than OSEMbreast and PSFbreast with median (interquartile range) equal to 130 (43–271), 316 (167–1042), 367 (107–1221), respectively (p < 0.0001). Therefore, using LifeX software, 28 (59%), 46 (98%) and 42 (89%) patients were exploitable with PSFWholeBody, OSEMbreast and PSFbreast, respectively.On matched comparisons, PSFbreast reconstruction presented better abilities than PSFwholeBody and OSEMbreast for the classification of luminal versus non-luminal breast tumours with an accuracy reaching 85.7% as compared to 67.8% for PSFwholeBody and 73.8% for OSEMbreast. PSFbreast accuracy, sensitivity, specificity, PPV and NPV were equal to 85.7%, 94.3%, 42.9%, 89.2%, 60.0%, respectively. Coarseness and ZLNU were found to be main variables of importance, appearing in all three prediction models. Coarseness was correlated with SUVmax on PSFwholeBody images (ρ = − 0.526, p = 0.005), whereas it was not on OSEMbreast (ρ = − 0.183, p = 0.244) and PSFbreast (ρ = − 0.244, p = 0.119) images. Moreover, the range of its values was higher on PSFbreast images as compared to OSEMbreast, especially in small lesions (MTV < 3 ml).ConclusionsHigh-resolution breast PET acquisitions, applying both small-voxel matrix and PSF modelling, appeared to improve the characterisation of breast tumours.Electronic supplementary materialThe online version of this article (10.1186/s13550-018-0466-5) contains supplementary material, which is available to authorized users.

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To seek for the minimal duration per bed position with a digital PET system without compromising image quality and lesion detection in patients requiring fast 18 F-FDG PET imaging. Materials and methods: 19 cancer patients experiencing pain or dyspnea and 9 pediatric patients were scanned on a Vereos system. List mode data were reconstructed with decreasing time frame down to 10 s per bed position. Noise was evaluated in the liver, blood pool and muscle, and using target-to-background ratios. Five PET readers recorded image quality, number of clinically relevant foci and of involved anatomical sites in reconstructions ranging from 60 to 10 s per bed position, compared to the standard 90 s reconstruction. Results: The following reconstructions, which harboured a noise not significantly higher than that of the standard reconstruction, were selected for clinical evaluation: 1iterations/10 subsets/20 sec (1i10 s20 sec ), 1i10 s30 sec , and 2i10 sPSF60 sec.Only the 60 s per bed acquisition displayed similar target-to-background ratios compared to the standard reconstruction, but mean ratios were still higher than 2.0 for the 30 s reconstruction. Inter-rater agreement for the number of involved anatomical sites and detected lesion was good or almost perfect (Kappa: 0.64−0.91) for all acquisitions. In particular, kappa for the 30 s per bed acquisition was 0.78 and 0.91 for lesion and anatomical sites number, respectively. Intra-rater agreement was also excellent for the 30 s reconstruction (kappa = 0.72). Median estimated total PET acquisition time for the 1i10 s30 sec , and the standard reconstruction were 4 and 12 min, respectively. Conclusions: Fast imaging is feasible with state-of-the-art PET systems. Acquisitions of 30 s per bed position are feasible with the Vereos system, requiring optimization of reconstruction parameters.