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Phantom and Clinical Evaluation of the Bayesian Penalized Likelihood Reconstruction Algorithm Q.Clear on an LYSO PET/CT System
Open accessAbstract
UNLABELLED: Q.Clear, a Bayesian penalized-likelihood reconstruction algorithm for PET, was recently introduced by GE Healthcare on their PET scanners to improve clinical image quality and quantification. In this work, we determined the optimum penalization factor (beta) for clinical use of Q.Clear and compared Q.Clear with standard PET reconstructions. METHODS: A National Electrical Manufacturers Association image-quality phantom was scanned on a time-of-flight PET/CT scanner and reconstructed using ordered-subset expectation maximization (OSEM), OSEM with point-spread function (PSF) modeling, and the Q.Clear algorithm (which also includes PSF modeling). Q.Clear was investigated for β (B) values of 100-1,000. Contrast recovery (CR) and background variability (BV) were measured from 3 repeated scans, reconstructed with the different algorithms. Fifteen oncology body (18)F-FDG PET/CT scans were reconstructed using OSEM, OSEM PSF, and Q.Clear using B values of 200, 300, 400, and 500. These were visually analyzed by 2 scorers and scored by rank against a panel of parameters (overall image quality; background liver, mediastinum, and marrow image quality; noise level; and lesion detectability). RESULTS: As β is increased, the CR and BV decreases; Q.Clear generally gives a higher CR and lower BV than OSEM. For the smallest sphere reconstructed with Q.Clear B400, CR is 28.4% and BV 4.2%, with corresponding values for OSEM of 24.7% and 5.0%. For the largest hot sphere, Q.Clear B400 yields a CR of 75.2% and a BV of 3.8%, with corresponding values for OSEM of 64.4% and 4.0%. Scorer 1 and 2 ranked B400 as the preferred reconstruction in 13 of 15 (87%) and 10 of 15 (73%) cases. The least preferred reconstruction was OSEM PSF in all cases. In most cases, lesion detectability was highest ranked for B200, in 9 of 15 (67%) and 10 of 15 (73%), with OSEM PSF ranked lowest. Poor lesion detectability on OSEM PSF was seen in cases of mildly (18)F-FDG-avid mediastinal nodes in lung cancer and small liver metastases due to background noise. Conversely, OSEM PSF was ranked second highest for lesion detectability in most pulmonary nodule evaluation cases. The combined scores confirmed B400 to be the preferred reconstruction. CONCLUSION: Our phantom measurement results demonstrate improved CR and reduced BV when using Q.Clear instead of OSEM. A β value of 400 is recommended for oncology body PET/CT using Q.Clear.
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“However, this difference is directly dependent on the user-defined penalization factor beta during PL reconstruction, and at beta values of 300 to 600, which have been rated as optimal for visual reading of [18F]FDG-PET images by human readers [107,112,113], inter-method SUV differences may no longer be significant [41,108,111].”
From Influences on PET Quantification and Interpretation (2022) · cites this paper as [107]
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“several authors have reported improved SNR and image quality with different radiopharmaceuticals, such as [18F]FDG [107,112,113], [18F]F-PSMA-1007 [143], [68Ga]Ga-PSMA-11 [169,170], [68Ga]Ga-DOTATATE [171] or 89Zr-labelled tracers [172].”
From Influences on PET Quantification and Interpretation (2022) · cites this paper as [107]
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“Using a commercially available PL reconstruction (General Electric [GE] Q.Clear), several phantom studies have shown that PL reconstruction can increase the CR of standardized sphere inserts compared to conventional TOF and PSF reconstruction [41,46,101,107,108].”
From Influences on PET Quantification and Interpretation (2022) · cites this paper as [107]
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Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
National electrical manufacturers association
Sokole, E. B., & Cradduck, T. D. (1926) · Journal of the A.I.E.E. · doi:10.1109/jaiee.1926.6537344 · Cited by 860
A Systematic Review of the Factors Affecting Accuracy of SUV Measurements
Adams, M. C., Turkington, T. G., Wilson, J. M., & Wong, T. Z. (2010) · American Journal of Roentgenology · doi:10.2214/ajr.10.4923 · Cited by 662
Kinahan, P. E., & Fletcher, J. W. (2010) · Seminars in Ultrasound, CT and MRI · doi:10.1053/j.sult.2010.10.001 · Cited by 631
Statistical methods for tomographic image reconstruction
Geman, S., & McClure, D. E. (1987) · Medical Entomology and Zoology · Cited by 579
Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls
Rahmim, A., Qi, J., & Sossi, V. (2013) · Medical Physics · doi:10.1118/1.4800806 · Cited by 371
Ahn, S., & Fessler, J. A. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.812251 · Cited by 345
Nuyts, J. L., Beque, D., Dupont, P. J., & Mortelmans, L. (2002) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.2002.998681 · Cited by 211
Influence of reconstruction iterations on 18F-FDG PET/CT standardized uptake values.
Jaskowiak, C., Bianco, J. A., Perlman, S. B., & Fine, J. P. (2005) · PubMed · Cited by 162
Teoh, E. J., McGowan, D. R., Bradley, K. M., Belcher, E., Black, E., & Gleeson, F. V. (2016) · European Radiology · doi:10.1007/s00330-015-3832-y · Cited by 109
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Research works printed but not linked: 2
- Ma, 2013, Clinical evaluation of penalized likelihood reconstruction in whole-body PET studies, Eur J Nucl Med Mol Imaging., 40, S109
- Kenny, 2013, Effect of the GE SharpIR PET reconstruction on contrast convergence and measured SUV values in the NEMA image quality phantom and patients, Eur J Nucl Med Mol Imaging., 40, S209
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Castiglioni, I., Rundo, L., Codari, M., Leo, G. D., Salvatore, C., Interlenghi, M., Gallivanone, F., Cozzi, A., D’Amico, N. C., & Sardanelli, F. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.006 · Cited by 779
Aide, N., Lasnon, C., Veit-Haibach, P., Sera, T., Sattler, B., & Boellaard, R. (2017) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-017-3740-2 · Cited by 344
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Hsu, D. F. C., Ilan, E., Peterson, W. T., Uribe, J., Lubberink, M., & Levin, C. S. (2017) · Journal of Nuclear Medicine · doi:10.2967/jnumed.117.189514 · Cited by 241
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van der Vos, C. S., Koopman, D., Rijnsdorp, S., Arends, A. J., Boellaard, R., van Dalen, J. A., Lubberink, M., Willemsen, A. T. M., & Visser, E. P. (2017) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-017-3727-z · Cited by 206
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Slomka, P. J., Pan, T., & Germano, G. (2015) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2015.09.006 · Cited by 178
Metadata
- Authors
- Teoh, E. J., McGowan, D. R., Macpherson, R. E., Bradley, K. M., & Gleeson, F. V.
- Affiliations
- Churchill Hospital (GB)
- Venue
- Journal of Nuclear Medicine
- Publisher
- Society of Nuclear Medicine
- Published
- 2015-07-09
- Volume
- 56
- Issue
- 9
- Pages
- 1447-1452
- ISSN
- 0161-5505 (ISSN-L)
- 2159-662X
- PMC
- PMC4558942
- OpenAlex
- W2161249702
- Cited by
- 265
- References
- 22
- Citations by year
- 2026: 14 · 2025: 30 · 2024: 26 · 2023: 32 · 2022: 27 · 2021: 39 · 2020: 28 · 2019: 21 · 2018: 16 · 2017: 19 · 2016: 9 · 2015: 2
- FWCI
- 12.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Nuclear Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Cancer Research UK (16466)
- National Institute for Health and Care Research (NIHR-HCS-D12-03-13)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Keywords
- Q.Clear, Bayesian penalized likelihood reconstruction, PET image reconstruction, OSEM, point spread function modeling, 18F-FDG PET/CT, image quality, lesion detectability, contrast recovery, background variation, time-of-flight PET, lung cancer, mediastinal lymph nodes, liver metastasis
- MeSH terms
- Machine Learning, Algorithms, Bayes Theorem, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Sensitivity and Specificity, Tomography, X-Ray Computed, Reproducibility of Results, Likelihood Functions, Phantoms, Imaging, Positron-Emission Tomography, Multimodal Imaging
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