Abstract
BackgroundThe 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.ResultsSubjective 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 SUVmax 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 (BSREM450) resulted in the best image quality for high dose 18F-FDG injections, whereas higher β-values (BSREM600) lead to the best image quality in low dose 18F-FDG PET/CT (p < 0.05).ConclusionsBSREM reconstruction algorithm used in digital detector PET leads to significant increases of lung tumor SUVmax, signal-to-background ratio, and signal-to-noise ratio, which translates into a higher image quality, tumor conspicuity, and image sharpness.
Highlights
The aim of this study was to evaluate and compare Positron-emission tomography (PET) image reconstruction algorithms on novel digital silicon photomultiplier PET/Computed tomography (CT) in patients with newly diagnosed and histopathologically confirmed lung cancer
Bayesian penalized likelihood reconstruction algorithm (BSREM) reconstruction algorithm used in digital detector PET leads to significant increases of lung tumor Maximum standardized uptake value (SUVmax), signal-to-background ratio, and signal-to-noise ratio, which translates into a higher image quality, tumor conspicuity, and image sharpness
A total of 45 patients (16 female, 29 male, mean age 68 ± 10 years) referred for the initial staging of lung cancer with 18F-FDG PET/CT participated in our study
Summary
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. There have been several technical advances within the last decade, including new hardware features, such as time-of-flight (TOF) acquisition [9] and silicon-based photodetectors (SIPM) as well as advanced image reconstruction methods, leading to an overall improvement of PET images. New reconstruction techniques, such as ordered subset expectation maximization (OSEM) and block sequential regularized expectation maximization (BSREM), came into clinical use and lead to a further improvement of image quality [11]
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