Background: The study aimed to evaluate the beta penalization factor of the BSREM reconstruction algorithm on a five-ring BGO-based PET CT system and compared it with conventional reconstructions.
Methods: Retrospective study involves 30 breast cancer patient data of 18F-fluorodeoxyglucose ( 18 F-FDG) PET CT for reconstruction with OSEM, OSEM + PSF, and BSREM under variable β factors ranging from 200 to 600 in the steps of 50. Liver noise, lesion SUVmax, SBR, and SNR for each reconstruction were calculated. Quantitative parameters of each beta factor of BSREM were compared with OSEM and OSEM + PSF, using the Wilcoxon sign rank test with Bonferroni correction, a value of P < 0.002 was considered statistically significant. Visual scoring by two readers was also evaluated.
Results: Thirty lesions of mean size 1.91 ± 0.58 cm range (0.7-3.6 cm) were identified. Liver noise and SBR were reduced, whereas SNR was increased with an increasing β value of BSREM. In comparison with OSEM, liver noise was not significantly different from β200 and β250. SNR of OSEM was significantly lower than any other β factors and SBR of β factor less than 500 was significantly higher than OSEM. In comparison with OSEM + PSF, liver noise was not significantly different from β400 and β350-500 do not show a significant difference in SNR and SBR compared with OSEM + PSF. β350 scored highest under visual scoring with a moderate agreement.
Conclusion: The study quantitatively indicates the optimum beta range of β250-450 and the qualitative evaluation indicates that β350 is an optimum beta factor of BSREM in breast cancer cases for 18 F-FDG WB-PET CT.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/MNM.0000000000001631 | DOI Listing |
Nihon Hoshasen Gijutsu Gakkai Zasshi
January 2025
Department of Radiology, Nara Prefecture General Medical Center.
Purpose: There are attempts to assess tumor heterogeneity by texture analysis. However, the ordered subsets-expectation maximization (OSEM) reconstruction method has problems depicting heterogeneities. The aim of this study was to identify image reconstruction parameters that improve the ability to depict internal tumor necrosis using a self-made phantom that simulates internal necrosis.
View Article and Find Full Text PDFAsia Ocean J Nucl Med Biol
January 2025
Department of Radiology, Faculty of Medicine, Shimane University, Izumo, Japan.
Objectives: We investigated image quality and standardized uptake values (SUVs) for different lesion sizes using clinical data generated by F-FDG-prone breast silicon photomultiplier (SiPM)-based positron emission tomography/computed tomography (PET/CT).
Methods: We evaluated the effect of point-spread function (PSF) modeling and Gaussian filtering (Gau) and determined the optimal reconstruction conditions. We compared the signal-to-noise ratio (SNR), contrast, %coefficient of variation (%CV), SUV, and Likert scale score between ordered-subset expectation maximization (OSEM) time-of-flight (TOF) and OSEM+TOF+PSF in phantom and clinical studies.
Med Phys
November 2024
High-Field and Hybrid MR Imaging, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
Med Phys
January 2025
Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.
Front Radiol
August 2024
Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia.
Introduction: The reconstruction of PET images involves converting sinograms, which represent the measured counts of radioactive emissions using detector rings encircling the patient, into meaningful images. However, the quality of PET data acquisition is impacted by physical factors, photon count statistics and detector characteristics, which affect the signal-to-noise ratio, resolution and quantitative accuracy of the resulting images. To address these influences, correction methods have been developed to mitigate each of these issues separately.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!