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Impact of a Bayesian penalized likelihood reconstruction algorithm on image quality in novel digital PET/CT: clinical implications for the assessment of lung tumors


Messerli, Michael; Stolzmann, Paul; Egger-Sigg, Michèle; Trinckauf, Josephine; D'Aguanno, Stefano; Burger, Irene A; von Schulthess, Gustav K; Kaufmann, Philipp A; Huellner, Martin W (2018). Impact of a Bayesian penalized likelihood reconstruction algorithm on image quality in novel digital PET/CT: clinical implications for the assessment of lung tumors. EJNMMI Physics, 5:27.

Abstract

BACKGROUND The aim of this study was to evaluate and compare PET image reconstruction algorithms on novel digital silicon photomultiplier PET/CT in patients with newly diagnosed and histopathologically confirmed lung cancer. A total of 45 patients undergoing 18F-FDG PET/CT for initial lung cancer staging were included. PET images were reconstructed using ordered subset expectation maximization (OSEM) with time-of-flight and point spread function modelling as well as Bayesian penalized likelihood reconstruction algorithm (BSREM) with different β-values yielding a total of 7 datasets per patient. Subjective and objective image assessment with all image datasets was carried out, including subgroup analyses for patients with high dose (> 2.0 MBq/kg) and low dose (≤ 2.0 MBq/kg) of 18F-FDG injection regimen.
RESULTS Subjective image quality ratings were significantly different among all different reconstruction algorithms as well as among BSREM using different β-values only (both p < 0.001). BSREM with a β-value of 600 was assigned the highest score for general image quality, image sharpness, and lesion conspicuity. BSREM reconstructions resulted in higher SUV of lung tumors compared to OSEM of up to + 28.0% (p < 0.001). BSREM reconstruction resulted in higher signal-/ and contrast-to-background ratios of lung tumor and higher signal-/ and contrast-to-noise ratio compared to OSEM up to a β-value of 800. Lower β-values (BSREM) resulted in the best image quality for high dose 18F-FDG injections, whereas higher β-values (BSREM) lead to the best image quality in low dose 18F-FDG PET/CT (p < 0.05).
CONCLUSIONS BSREM reconstruction algorithm used in digital detector PET leads to significant increases of lung tumor SUV, signal-to-background ratio, and signal-to-noise ratio, which translates into a higher image quality, tumor conspicuity, and image sharpness.

Abstract

BACKGROUND The aim of this study was to evaluate and compare PET image reconstruction algorithms on novel digital silicon photomultiplier PET/CT in patients with newly diagnosed and histopathologically confirmed lung cancer. A total of 45 patients undergoing 18F-FDG PET/CT for initial lung cancer staging were included. PET images were reconstructed using ordered subset expectation maximization (OSEM) with time-of-flight and point spread function modelling as well as Bayesian penalized likelihood reconstruction algorithm (BSREM) with different β-values yielding a total of 7 datasets per patient. Subjective and objective image assessment with all image datasets was carried out, including subgroup analyses for patients with high dose (> 2.0 MBq/kg) and low dose (≤ 2.0 MBq/kg) of 18F-FDG injection regimen.
RESULTS Subjective image quality ratings were significantly different among all different reconstruction algorithms as well as among BSREM using different β-values only (both p < 0.001). BSREM with a β-value of 600 was assigned the highest score for general image quality, image sharpness, and lesion conspicuity. BSREM reconstructions resulted in higher SUV of lung tumors compared to OSEM of up to + 28.0% (p < 0.001). BSREM reconstruction resulted in higher signal-/ and contrast-to-background ratios of lung tumor and higher signal-/ and contrast-to-noise ratio compared to OSEM up to a β-value of 800. Lower β-values (BSREM) resulted in the best image quality for high dose 18F-FDG injections, whereas higher β-values (BSREM) lead to the best image quality in low dose 18F-FDG PET/CT (p < 0.05).
CONCLUSIONS BSREM reconstruction algorithm used in digital detector PET leads to significant increases of lung tumor SUV, signal-to-background ratio, and signal-to-noise ratio, which translates into a higher image quality, tumor conspicuity, and image sharpness.

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Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Institute of Pathology and Molecular Pathology
04 Faculty of Medicine > University Hospital Zurich > Clinic for Nuclear Medicine
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:26 September 2018
Deposited On:04 Dec 2018 14:01
Last Modified:04 Dec 2018 14:04
Publisher:SpringerOpen
ISSN:2197-7364
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1186/s40658-018-0223-x
PubMed ID:30255439

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