Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Oct 20;10(1):65.
doi: 10.1186/s40658-023-00584-1.

Optimization of Q.Clear reconstruction for dynamic 18F PET imaging

Elisabeth Kirkeby Lysvik  1   2 , Lars Tore Gyland Mikalsen  3   4 , Mona-Elisabeth Rootwelt-Revheim  5   6   7 , Kyrre Eeg Emblem  3 , Trine Hjørnevik  3   5
Affiliations

Optimization of Q.Clear reconstruction for dynamic 18F PET imaging

Elisabeth Kirkeby Lysvik et al. EJNMMI Phys. .

Abstract

Background: Q.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a β penalization factor. This study aimed to determine the optimal β-factor for accurate quantitation of dynamic PET scans.

Methods: A Flangeless Esser PET Phantom with eight hollow spheres (4-25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different β-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CVRC) and root-mean-square error (RMSERC) were evaluated for the phantom data. Two male patients with recurrent glioblastoma were scanned on the same scanner using 18F-PSMA-1007. Using an irreversible two-tissue compartment model, the area under curve (AUC) and the net influx rate Ki were calculated to assess the impact of different β-factors on the pharmacokinetic analysis of clinical PET brain data.

Results: In general, RC and CVRC decreased with increasing β-factor in the phantom data. For small spheres (< 10 mm), and in particular for short acquisition times, low β-factors resulted in high variability and an overestimation of measured activity. Increasing the β-factor improves the variability, however at a cost of underestimating the measured activity. For the clinical data, AUC decreased and Ki increased with increased β-factor; a change in β-factor from 300 to 1000 resulted in a 25.5% increase in the Ki.

Conclusion: In a complex dynamic dataset with variable acquisition times, the optimal β-factor provides a balance between accuracy and precision. Based on our results, we suggest a β-factor of 300-500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher β-factors.

Trial registration: Clinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142 . EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO .

Keywords: Dynamic PET; Q.Clear; Quantitation; Recovery coefficient; β-factor.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Example delineations of VOIs for each sphere shown on the CT (a) and PET image (b) in all three directions
Fig. 2
Fig. 2
Standardized uptake value (SUV) image of a representative male adult patient with recurrent glioblastoma scanned with 18F-PSMA-1007. 18F-PSMA-1007 uptake and delineated VOIs are shown for tumour tissue (white arrow in a) and blood pool (white arrow in b)
Fig. 3
Fig. 3
RC (mean of five measurements) versus β-factor for various sphere sizes and acquisition times. The red dotted line represents a ratio of measured and known concentration of 1
Fig. 4
Fig. 4
CVRC versus β-factor for various sphere sizes and acquisition times
Fig. 5
Fig. 5
RMSERC versus β-factor for all spheres (blue), 10–25 mm spheres (red) and 4–7.9 mm spheres (yellow). The dotted lines represent a 10% change from the lowest RMSERC value
Fig. 6
Fig. 6
Time activity curves (TACs) for the blood pool and tumour VOIs are shown in panels a and c. AUC of blood and tumour TACs are shown in panels b and d for β-factors 300–1000 for both patients
See this image and copyright information in PMC

References

    1. Croteau E, Lavallée E, Labbe SM, Hubert L, Pifferi F, Rousseau JA, et al. Image-derived input function in dynamic human PET/CT: methodology and validation with 11C-acetate and 18F-fluorothioheptadecanoic acid in muscle and 18F-fluorodeoxyglucose in brain. Eur J Nucl Med Mol Imaging. 2010;37(8):1539–1550. doi: 10.1007/s00259-010-1443-z. - DOI - PMC - PubMed
    1. Rausch I, Cal-González J, Dapra D, Gallowitsch HJ, Lind P, Beyer T, et al. Performance evaluation of the Biograph mCT Flow PET/CT system according to the NEMA NU2-2012 standard. EJNMMI Phys. 2015;2(1):26. doi: 10.1186/s40658-015-0132-1. - DOI - PMC - PubMed
    1. Pan T, Einstein SA, Kappadath SC, Grogg KS, Lois Gomez C, Alessio AM, et al. Performance evaluation of the 5-Ring GE Discovery MI PET/CT system using the national electrical manufacturers association NU 2–2012 Standard. Med Phys. 2019;46(7):3025–3033. doi: 10.1002/mp.13576. - DOI - PMC - PubMed
    1. Chicheportiche A, Marciano R, Orevi M. Comparison of NEMA characterizations for discovery MI and discovery MI-DR TOF PET/CT systems at different sites and with other commercial PET/CT systems. EJNMMI Phys. 2020;7(1):4. doi: 10.1186/s40658-020-0271-x. - DOI - PMC - PubMed
    1. Krejza J, Arkuszewski M, Kasner SE, Weigele J, Ustymowicz A, Hurst RW, et al. Carotid artery diameter in men and women and the relation to body and neck size. Stroke. 2006;37(4):1103–1105. doi: 10.1161/01.STR.0000206440.48756.f7. - DOI - PubMed

Associated data