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Master Thesis

How fast can we scan patients with modern (digital) PET/CT systems?

Abstract : Purpose: To seek the minimal duration per bed position with a digital PET system without compromising image quality and lesion detection in patients requiring fast 18F-FDG PET imaging. Materials and methods: 28 patients were scanned on a Vereos system. List mode data were reconstructed with decreasing time frame down to 10sec per bed position. Noise was evaluated in the liver, blood pool and muscle, and using target-to-background ratios. 5 PET readers recorded image quality, number of clinically relevant foci and of involved anatomical sites in reconstructions ranging from 60 to 10sec per bed position, compared to the standard 90sec reconstruction. Results: The following reconstructions, which harboured a noise not significantly higher than the standard reconstruction, were selected: 1iteration/10subsets/20sec (1i10s20sec), 1i10s30sec, and 2i10sPSF60sec. Only the 60sec per bed displayed similar target-to-background ratios compared to the standard reconstruction, but mean ratios were still higher than 2.0 for the 30sec reconstruction. Interrater agreement for the number of involved anatomical sites and detected lesion was good or almost perfect (Kappa: 0.64-0.91). In particular, kappa for the 30sec per bed acquisition was 0.78 and 0.91 for lesion and anatomical sites number, respectively. Intra-rater agreement was also excellent for the 30s reconstruction. Estimated total PET acquisition time for the 1i10s30sec, and the standard reconstruction were 4 and 12 min, respectively. Conclusion: Fast imaging is feasible with state-of-the-art PET systems. Acquisitions of 30s per bed position are feasible with the Vereos system, requiring optimization of reconstruction parameters.
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Submitted on : Thursday, February 18, 2021 - 3:39:40 PM
Last modification on : Wednesday, March 17, 2021 - 3:32:36 AM


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  • HAL Id : dumas-03145743, version 1


Nicolas Coudrais. How fast can we scan patients with modern (digital) PET/CT systems?. Human health and pathology. 2020. ⟨dumas-03145743⟩



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