The influence of Q.Clear reconstruction on the contrast recovery coefficient and semi-quantitative parameters of NEMA phantom imaging

• Autorzy: Skórkiewicz, Konrad , Łątka, Kazimierz , Sowa-Staszczak, Anna , Hubalewska-Dydejczyk, Alicja
• Języki publikacji: EN

Abstrakty

EN

Background: The aim of the study is to optimise the value of B parameter (β), which is used in the Q.Clear reconstruction in the imaging of neuroendocrine tumours. The study is divided into two parts: analysis of phantom data aiming at selection of the appropriate β for small changes, and then assessment of its impact on the quality of patients' images. The literature data on the optimal β value are inconclusive. Furthermore, the suggested values are not the result of the semi-quantitative assessment of Standard Uptake Volume (SUV) or the proper verification based on, for example, phantom studies using the known activity. Results: The obtained results show that β increase raises the image uniformity in the Q.Clear reconstruction algorithm. Also, referring to the scientific reports, one can see that the signal to noise ratio in the image increases. The effect of the β change on the SUV mean and Contrast Recovery Coefficient (CRC) value is greatest for the smallest objects. The decrease of this parameter is also much higher with lower values of activity (a lower counts statistic in the PET system). Conclusions: An increase of β has an adverse effect on the quality of a semi-quantitative assessment of SUV -as the parameter increases, the SUV and CRC values decrease. In the visual assessment, a satisfactory image quality is present with β = 450. Based on the analysis of SUV and CRC, an appropriate range of β values was selected as 350-450. At the selected range, a retrospective analysis of the clinical images of neuroendocrine tumours will be performed in the future and the impact of the change on the semi-quantitative analysis of pathological changes will be verified.

Słowa kluczowe

EN

Q.Clear; reconstruction algorithm; NEMA phantom

• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2023
• Tom: Vol. 19, no. 1
• Strony: 17--22
• Opis fizyczny: Bibliogr. 15 poz., rys.

Twórcy

autor

Skórkiewicz, Konrad

• Department of Medical Physics, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Krakow, Poland
• Department of Medical Physics and Radiation Protection, University Hospital, Krakow, Poland
• Department of Biophysics, Faculty of Medicine, Jagiellonian University Medical College, Krakow, Poland
• Center for Theranostics, Jagiellonian University, Krakow, Poland

autor

Łątka, Kazimierz

• Department of Medical Physics, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Krakow, Poland

autor

Sowa-Staszczak, Anna

• Endocrinology Department, Jagiellonian University Medical College, Krakow, Poland

autor

Hubalewska-Dydejczyk, Alicja

• Endocrinology Department, Jagiellonian University Medical College, Krakow, Poland

Bibliografia

• 1. Tong S, Alessio A, Kinahan P. Image reconstruction for PET/CT scanners: past achievements and future challenges. Imaging Med. 2010;2:529-45.
• 2. Vennart J, Bird N, Buscombe J, Cheow HK, Nowosinska E, Heard S. Optimization of PET/CT image quality using the GE ‘Sharp IR’point-spread function reconstruction algorithm. Nuc Med Commun. 2017;38:471-9.
• 3. Ross S, Q.Clear, GE Healthcare.
• 4. Otani T, Hosono M, Kanagaki M. Clinical evaluation and optimization of Q.Clear; a new PET reconstruction algorithm. J Nuc Med. 2017;58(Suppl 1):575.
• 5. Reddy R, Hainer J, Sticka W, Park M-A. Evaluation of different reconstruction algorithms in contrast recovery for noncentrally located small lesions. J. Nucl. Med. 2018;59(Suppl 1):1775.
• 6. Andersen T, Flemming Hoilund-Carlsen P. The Q.Clear PET reconstruction algorithm: Evaluation using the NEMA IQ Phantom. J. Nucl. Med. 2016;57(Suppl 2):1973.
• 7. Guo B, Wu Z, Zhao B, Huang B, Li X, Zhao J, et al. Improved Quantification Accuracy Using Bayesian Penalized Likelihood Based Reconstruction on 68Ga PET-CT. J. Nucl. Med. 2020;61(Suppl 1):162.
• 8. Bai B, Bading J, Conti P. Tumor Quantification in Clinical Positron Emission Tomography. Theranostics 2013;3(10):787-801.
• 9. National Electrical Manufacturers Association, NEMA Standards Publication NU 2-2018 Performance Measurements of Positron Emission Tomographs (PETS), 1300 N. 17th Street, Suite 900 Rosslyn, VA 22209.
• 10. Hyun OJ, Lodge M, Wahl R. Practical PERCIST: A Simplified Guide to PET Response Criteria in Solid Tumors 1.01. Radiology 2016;280(2):576-84.
• 11. Tian D, Yang H, Li Y, Cui B, Lu J. The effect of Q.Clear reconstruction on quantification and spatial resolution of 18F-FDG PET in simultaneous PET/MR. EJNMMI Phys. 2022;9:1.
• 12. Manual for the PET-CT Discovery_MI DR device, GE Healthcare.
• 13. Rogasch MJ, Suleiman S, Hofheinz F, Bluemel S, Lukas M, Amthauer H, et al. Reconstructed spatial resolution and contrast recovery with Bayesian penalized likelihood reconstruction (Q.Clear) for FDG-PET compared to time-of-flight (TOF) with point spread function (PSF). EJNMMI Phys. 2020;7:2.
• 14. Rijnsdorp S, Roef JM, Arends JA. Impact of the Noise Penalty Factor on Quantification in Bayesian Penalized Likelihood (Q.Clear) Reconstructions of 68Ga-PSMA PET/CT Scans. Diagnostics. 2021;11:847.
• 15. Teoh JE, McGowan RD, Macpherson ER, Bradley KM, Gleeson FV. Phantom and clinical evaluation of the Bayesian penalized likelihood reconstruction algorithm Q.Clear on an LYSO PET/CT system. J. Nucl. Med. 2015;56(9):1447-52.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Identyfikatory

DOI

10.5604/01.3001.0054.1818