The DOI matches its Crossref record; 21 of the 22 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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 586
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
Self-citation
Flagged first, then by citations
1 of the 22 references couldn’t be matched to a known paper.
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
Cited by
Sorted by citations
Items 201–265 of 265
Hussain, A. A., Forssell-Aronsson, E., Rosholm, T., & Mehrara, E. (2024) · Brazilian Journal of Radiation Sciences · doi:10.15392/2319-0612.2024.2487 · Cited by 1
Is There Novel 18 F-FDG Biodistribution in the Digital PET/CT Era? A Real-World Data Analysis
Babacan, G. B., Özülker, F., Şahin, O., Güven, O., Kanatsız, O., Alçın, G., & Özülker, T. (2025) · Cancer Biotherapy and Radiopharmaceuticals · doi:10.1089/cbr.2024.0226 · Cited by 1
Bonney, L. M., Walker, M. D., & McGowan, D. R. (2025) · EJNMMI Physics · doi:10.1186/s40658-025-00722-x · Cited by 1
Self-citationBiodistribution of [18F]BCPP-BF in humans: a first-in-human positron emission tomography study
Sato, K., Bunai, T., Oda, A., Ohba, H., Sano, Y., Tokui, A., Tsukada, H., Nishizawa, S., Harada, N., & Ouchi, Y. (2025) · Nuclear Medicine and Biology · doi:10.1016/j.nucmedbio.2025.109019 · Cited by 1
Mikami, S., Nakamura, F., Nakamura, A., & Torizuka, T. (2021) · Japanese Journal of Radiological Technology · doi:10.6009/jjrt.2021_jsrt_77.5.454 · Cited by 1
Dziuk, M., Witkowska-Patena, E., Gizewska, A., Mazurek, A., Pieczonka, A., Koza, M., Gerszewska, M., Podgajny, Z., & Chojnowska, M. (2022) · Nuclear Medicine Review · doi:10.5603/nmr.a2022.0009 · Cited by 1
Huellner, M. W., Pizzuto, D. A., Mächler, P. C., & Sekine, T. (2020) · Springer eBooks · doi:10.1007/978-3-030-48419-4_20 · Cited by 1
Digitale SPECT und PET: Klinische Konsequenzen
Rogasch, J. M. M. (2022) · Angewandte Nuklearmedizin · doi:10.1055/a-1715-5305 · Cited by 1
Basic Principles of Hybrid Imaging
Lorenzon, L., Bonelli, M., Fracchetti, A., & Ferrari, P. (2023) · doi:10.1002/9781119603627.ch2 · Cited by 1
Vieira, T. S., Gomes, M. T., Faria, D. B., Barroso, S., & de Oliveira, J. P. (2018) · Journal of Nuclear Medicine & Radiation Therapy · doi:10.4172/2155-9619.1000376
Ahn, S., Wangerin, K. A., Wollenweber, S. D., Ross, S. G., Stearns, C. W., & Kinahan, P. E. (2017) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.2017.8532858
Improved image reconstruction of
Kirchner, J., O’Donoghue, J. A., Becker, A. S., & Ulaner, G. A. (2021)
Bjöersdorff, M., Puterman, C., Oddstig, J., Amidi, J., Zackrisson, S., Kjölhede, H., Bjartell, A. S., Wollmer, P., & Trägårdh, E. (2021) · Research Square · doi:10.21203/rs.3.rs-442608/v1
Case, J. A. (2021) · Current Cardiology Reports · doi:10.1007/s11886-021-01507-1
Bjöersdorff, M., Puterman, C., Oddstig, J., Amidi, J., Zackrisson, S., Kjölhede, H., Bjartell, A. S., Wollmer, P., & Trägårdh, E. (2021) · Research Square · doi:10.21203/rs.3.rs-552089/v1
Maronnier, Q., Courbon, F., & Caselles, O. (2021) · Research Square · doi:10.21203/rs.3.rs-960402/v1
Transverse momentum distribution of secondary $pi$$sup 0$ mesons in air jet
Nishio, A. (1973) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)
Ren, T., Nedzvedz, O., Ye, F., Jiang, J., Nedzved, A., Gurevich, I. B., & Yashina, V. V. (2021) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661821040180
Twyman, R., Arridge, S., Kereta, Ž., Jin, B., Brusaferri, L., Ahn, S., Stearns, C. W., Hutton, B. F., Burger, I. A., Kotasidis, F. A., et al. (2022) · doi:10.36227/techrxiv.19497680.v1
Twyman, R., Arridge, S., Kereta, Ž., Jin, B., Brusaferri, L., Ahn, S., Stearns, C. W., Hutton, B. F., Burger, I. A., Kotasidis, F. A., et al. (2022) · doi:10.36227/techrxiv.19497680
Wu, Z., Qin, Z., Huang, B., Guo, B., Hao, X., Wu, P., Zhao, B., Xie, J., & Li, S. (2020) · doi:10.21203/rs.3.rs-16660/v1
Matheoud, R., Al-Maymani, N., Oldani, A., Sacchetti, G. M., Brambilla, M., & Carriero, A. (2020) · doi:10.21203/rs.3.rs-17743/v1
Liu, H., Liu, L., Xu, S., Zhonglin, L. U., Mok, G. S. P., Wang, Y., Yi, T., & Chen, Y. (2022) · The Quarterly Journal of Nuclear Medicine and Molecular Imaging · doi:10.23736/s1824-4785.22.03409-4
Subesinghe, M., Ilyas, H., Dunn, J., Mir, N., Duran, A., Mikhaeel, N. G., & Barrington, S. F. (2022) · Clinical Radiology · doi:10.1016/j.crad.2022.09.124
Siekkinen, R., Han, C., Maaniitty, T., Teräs, M., Knuuti, J. M., Saraste, A., & Teuho, J. (2023) · Journal of Nuclear Cardiology · doi:10.1007/s12350-022-03164-5
Digitale SPECT und PET: Klinische Konsequenzen
Rogasch, J. M. M. (2023) · Radiopraxis · doi:10.1055/a-1993-7420
Odagiri, H., Takanami, K., Kaneta, T., Usui, A., Kojima, I., Kodama, H., Saitou, S., Tanaka, Y., Dendo, Y., & Takase, K. (2023) · The Tohoku Journal of Experimental Medicine · doi:10.1620/tjem.2023.j026
Li, L., Chen, R., Wen, J., Yang, X., Hu, D., Sun, H., Ge, Q., Ng, Y. L., Zhou, Y., Wan, L., et al. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-22-1341
Genc, M., Yıldırım, N., Coskun, N., Özdemir, E., & Türkölmez, Ş. (2023) · Revista Española de Medicina Nuclear e Imagen Molecular · doi:10.1016/j.remn.2023.07.002
Akil, S., Székely, A. E., Hedeer, F., Olsson, B., Engblom, H., & Hindorf, C. (2023) · Clinical Physiology and Functional Imaging · doi:10.1111/cpf.12861
Yamagata, T., Fukuchi, K., Haramiishi, K., & Fukuchi, K. (2024) · Research Square · doi:10.21203/rs.3.rs-3955385/v1
Praktischer Leitfaden für die technische Konfiguration
Mohnike, W., Ponath, H.-C., Krüwel, T., & Bako, A. (2024) · Springer eBooks · doi:10.1007/978-3-662-67192-4_5
Yamashita, Y., HIRAKAWA, K., Yoshidome, S., & Awamoto, S. (2024) · Japanese Journal of Radiological Technology · doi:10.6009/jjrt.25-1518
Ting, M., McDermott, G. M., Zarei, A., Patel, C., Chowdhury, F. U., Rodrigues, A., & Scarsbrook, A. F. (2025) · Nuclear Medicine Communications · doi:10.1097/mnm.0000000000001957
Sakamoto, Y., Yamamoto, Y., & Uegaki, T. (2025) · Japanese Journal of Radiological Technology · doi:10.6009/jjrt.25-1453
Optimization of scan time in 18F FDG-PET/CT imaging for cardiac sarcoidosisPreprint
Takahashi, Y., Tsuchitani, T., Miki, R., Nakagiri, H., Sakuma, T., Morotomi, R., Matsumoto, K., & Kitajima, K. (2025) · Research Square · doi:10.21203/rs.3.rs-7221127/v1
Kojima, I., Takanami, K., Watanabe, N., Kodama, H., Odagiri, H., & Iikubo, M. (2025) · Dentomaxillofacial Radiology · doi:10.1093/dmfr/twaf058
Bilateral Low-Accumulation Bladder Artifacts Due to High Radioactivity of Urine on 18 F-FDG PET/CT
Ichikawa, Y., Tomita, Y., Hashizume, K., & Sakuma, H. (2025) · Journal of Nuclear Medicine Technology · doi:10.2967/jnmt.125.270294
Liang, M., Cui, C., Li, X., Guo, J., Wang, X., Hu, Y., Yan, C., Shi, X., Li, M., Cao, J., et al. (2025) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-025-07604-5
Kirchner, J., O’Donoghue, J. A., Becker, A. S., & Ulaner, G. A. (2021) · Figshare · doi:10.6084/m9.figshare.13612644
Bonardel, G., Dupont, A., Decazes, P., Queneau, M., Modzelewski, R., Coulot, J., Calvez, N. L., & Hapdey, S. (2022) · Figshare · doi:10.6084/m9.figshare.19753308
Kirchner, J., O’Donoghue, J. A., Becker, A. S., & Ulaner, G. A. (2021) · Figshare · doi:10.6084/m9.figshare.13612647
Kirchner, J., O’Donoghue, J. A., Becker, A. S., & Ulaner, G. A. (2021) · Figshare · doi:10.6084/m9.figshare.13612641
Wagatsuma, K., Sakata, M., Miwa, K., Hamano, Y., Kawakami, H., Kamitaka, Y., Yamao, T., Miyaji, N., Ishibashi, K., 多胡哲郎, et al. (2024) · Figshare · doi:10.6084/m9.figshare.25670103
Lindström, E., Lindsjö, L., Sundin, A., Sörensen, J., & Lubberink, M. (2020) · Figshare · doi:10.6084/m9.figshare.12487391
Dedja, M., Mehranian, A., Bradley, K. M., Walker, M. D., Fielding, P. A., Wollenweber, S. D., Johnsen, R., & McGowan, D. R. (2024) · Figshare · doi:10.6084/m9.figshare.26693737
Self-citationAyati, N., McIntosh, L., Buteau, J. P., Alipour, R., Pudís, M., Daw, N., Jackson, P. A., & Hofman, M. S. (2024) · Figshare · doi:10.6084/m9.figshare.25762911
Xu, L., Li, R., Wu, R., Yang, R., You, Q.-Q., Yao, X.-C., Xie, H.-F., Lv, Y., Dong, Y., Wang, F., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19451626
Nükleer Tıp Hibrit Görüntülemede Görüntü Kalitesine Etki Eden Faktörler ve Görüntü Optimizasyonu
Çakır, F. (2025) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1104.c4420
Iimori, T., Kamiya, T., Maeda, Y., Odagiri, H., Iguchi, H., Yada, N., & Narita, M. (2026) · Nuclear Medicine Communications · doi:10.1097/mnm.0000000000002116
Deep learning-enhanced data-driven gating improves FDG PET/CT clinical image quality
Wilson, Z., Dedja, M., Su, K., Johnsen, R., Bradley, K. M., & McGowan, D. R. (2026) · EJNMMI Physics · doi:10.1186/s40658-026-00851-x
Self-citationQ.Clear reconstruction shortening the scanning time for 11C-CFT brain PET/MR imaging
Chen, L., Gong, W., Wang, D., Long, H., Li, X. C., Xia, R., Yao, Y., Zhou, W., Zhou, W., & Chen, X. (2026) · EJNMMI Physics · doi:10.1186/s40658-026-00878-0
Fukai, S., Daisaki, H., Onda, R., 志賀真奈美, Yoshida, H., Motegi, K., Shimada, N., Takatsu, K., Nakagawa, M., & Terauchi, T. (2026) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-026-01742-2
Wilson, Z., Kuhar, M., Su, K., Johnsen, R., Bradley, K. M., & McGowan, D. R. (2026) · EJNMMI Physics · doi:10.1186/s40658-026-00896-y
Self-citationHadjitheodorou, P., Sotiriou, M., Kyrou, K., Adamou, G., Eleftheriou, M., Konstantinidou, K., & Vrachimis, A. (2026) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2026.06.002
Matsubara, K., Ibaraki, M., Hashimoto, F., Onishi, Y., Shinohara, Y., Sato, K., Yamamoto, H., & Kinoshita, T. (2026) · Physica Medica · doi:10.1016/j.ejmp.2026.105841
Hansen, T. M., Christensen, N. L., & Vendelbo, M. H. (2026) · EJNMMI Physics · doi:10.1186/s40658-026-00917-w
Key Technologies in PET Instrumentation and Image Generation
Sitek, A., Klyuzhin, I. S., Farhadi, F., & Rahmim, A. (2026) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2026.07.001
Piai, A., Bordigoni, B., Antunovic, L., Busnardo, E., Canevari, C., Fallanca, F., Magnani, P., Mapelli, P., Chiti, A., & Vecchio, A. D. (2026) · Physica Medica · doi:10.1016/j.ejmp.2026.107197
Shiiba, T., Tanahashi, S., Takasu, K., Hamajima, Y., Yamashita, K., & Tsujimoto, M. (2026) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-026-01807-2
Principe et technique de la tomographie par émission de positons couplée à la tomodensitométrie
Soret, M., & Maisonobe, J.-A. (2026) · EMC - Radiologie et imagerie médicale - Principes et techniques - Radioprotection · doi:10.1016/s1879-8497(25)49588-0
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review