Cited by 265Medical Imaging Techniques and ApplicationsRadiation Dose and ImagingCardiac Imaging and Diagnostics

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Phantom and Clinical Evaluation of the Bayesian Penalized Likelihood Reconstruction Algorithm Q.Clear on an LYSO PET/CT System

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Teoh, Eugene J., McGowan, Daniel R., Macpherson, Ruth E., Bradley, Kevin M., Gleeson, Fergus V.·2015·Journal of Nuclear Medicine

DOI 10.2967/jnumed.115.159301PMC PMC4558942

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Abstract

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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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
Positron Emission Tomography-Computed Tomography Standardized Uptake Values in Clinical Practice and Assessing Response to Therapy
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
Globally convergent image reconstruction for emission tomography using relaxed ordered subsets algorithms
Ahn, S., & Fessler, J. A. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.812251 · Cited by 345
A concave prior penalizing relative differences for maximum-a-posteriori reconstruction in emission tomography
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
Novel penalised likelihood reconstruction of PET in the assessment of histologically verified small pulmonary nodules
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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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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Authors: Teoh, E. J., McGowan, D. R., Macpherson, R. E., Bradley, K. M., & Gleeson, F. V.
Affiliations: Churchill Hospital (GB)
ORCID: Teoh, Eugene J.
McGowan, Daniel R.
Macpherson, Ruth E.
Bradley, Kevin M.
Gleeson, Fergus V.
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
DOI: 10.2967/jnumed.115.159301
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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Phantom and Clinical Evaluation of the Bayesian Penalized Likelihood Reconstruction Algorithm Q.Clear on an LYSO PET/CT System

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