Impact of penalizing factor in a block-sequential regularized expectation maximization reconstruction algorithm for 18F-fluorocholine PET-CT regarding image quality and interpretation
Open accessAbstract
Recently, the block-sequential regularized expectation maximization (BSREM) reconstruction algorithm was commercially introduced (Q.Clear, GE Healthcare, Milwaukee, WI, USA). However, the combination of noise-penalizing factor (β), acquisition time, and administered activity for optimal image quality has not been established for 18 F-fluorocholine (FCH). The aim was to compare image quality and diagnostic performance of different reconstruction protocols for patients with prostate cancer being examined with 18 F-FCH on a silicon photomultiplier-based PET-CT. Thirteen patients were included, injected with 4 MBq/kg, and images were acquired after 1 h. Images were reconstructed with frame durations of 1.0, 1.5, and 2.0 min using β of 150, 200, 300, 400, 500, and 550. An ordered subset expectation maximization (OSEM) reconstruction with a frame duration of 2.0 min was used for comparison. Images were quantitatively analyzed regarding standardized uptake values (SUV) in metastatic lymph nodes, local background, and muscle to obtain contrast-to-noise ratios (CNR) as well as the noise level in muscle. Images were analyzed regarding image quality and number of metastatic lymph nodes by two nuclear medicine physicians. The highest median CNR was found for BSREM with a β of 300 and a frame duration of 2.0 min. The OSEM reconstruction had the lowest median CNR. Both the noise level and lesion SUV max decreased with increasing β. For a frame duration of 1.5 min, the median quality score was highest for β 400-500, and for a frame duration of 2.0 min the score was highest for β 300-500. There was no statistically significant difference in the number of suspected lymph node metastases between the different image series for one of the physicians, and for the other physician the number of lymph nodes differed only for one combination of image series. To achieve acceptable image quality at 4 MBq/kg 18 F-FCH, we propose using a β of 400-550 with a frame duration of 1.5 min. The lower β should be used if a high CNR is desired and the higher if a low noise level is important.
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Several studies on subjective image quality in whole-body [18F]FDG-PET with PL reconstruction found that image quality was highest at beta values of 450 (to 600) despite lower lesion contrast recovery or image sharpness at these beta values compared to lower values.
“Several studies on subjective image quality in whole-body [18F]FDG-PET with PL reconstruction found that image quality was highest at beta values of 450 (to 600) despite lower lesion CR or “image sharpness” at these beta values compared to lower values [41,108,112,175,179].”
From Influences on PET Quantification and Interpretation (2022) · cites this paper as [175]
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Experimental — accuracy not yet measuredIn a small sample of 13 patients undergoing [18F]fluorocholine PET/CT for prostate cancer staging, PL with different beta values showed a comparable number of positive lymph nodes to that revealed by OSEM with PSF and TOF.
“In contrast, in a small sample of 13 patients undergoing [18F]fluorocholine PET/CT for prostate cancer staging, PL with different beta values showed a comparable number of positive lymph nodes to that revealed by OSEM with PSF and TOF [175].”
From Influences on PET Quantification and Interpretation (2022) · cites this paper as [175]
Methods (Clinical data and Imaging data) report 13 patients with high-risk prostate cancer and compare reconstructions with different β values against OSEM with point-spread function and time-of-flight. Results (Qualitative analysis) find largely comparable numbers of suspected metastatic lymph nodes, but Figure 6 says that assessment covered ten patients, not all 13; the nodes were suspected rather than confirmed positive. (LLM, conf=0.88)
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Cites
Molecular imaging of cancer with positron emission tomography
Gambhir, S. S. (2002) · Nature reviews. Cancer · doi:10.1038/nrc882 · Cited by 1,658 · see citing works
Teoh, E. J., McGowan, D. R., Macpherson, R. E., Bradley, K. M., & Gleeson, F. V. (2015) · Journal of Nuclear Medicine · doi:10.2967/jnumed.115.159301 · Cited by 265 · see citing works
Application of Silicon Photomultipliers to Positron Emission Tomography
Roncali, E., & Cherry, S. R. (2011) · Annals of Biomedical Engineering · doi:10.1007/s10439-011-0266-9 · Cited by 242 · see citing works
Studies of a Next-Generation Silicon-Photomultiplier–Based Time-of-Flight PET/CT System
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 · see citing works
Quantification, improvement, and harmonization of small lesion detection with state-of-the-art PET
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 · see citing works
Fast EM-like methods for maximum "a posteriori" estimates in emission tomography
Pierro, A. R. D., & Yamagishi, M. E. B. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.921477 · Cited by 182 · see citing works
Feasibility of state of the art PET/CT systems performance harmonisation
Kaalep, A., Sera, T., Rijnsdorp, S., Yaqub, M., Talsma, A., Lodge, M. A., & Boellaard, R. (2018) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-018-3977-4 · Cited by 160 · see citing works
Ahn, S., Ross, S. G., Asma, E., Miao, J., Jin, X. H., Cheng, L., Wollenweber, S. D., & Manjeshwar, R. M. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/15/5733 · Cited by 154 · see citing works
18F-fluorocholine for prostate cancer imaging: a systematic review of the literature
Bauman, G. S., Belhocine, T., Kovacs, M. S., Ward, A. D., Beheshti, M., & Rachinsky, I. (2011) · Prostate Cancer and Prostatic Diseases · doi:10.1038/pcan.2011.35 · Cited by 150 · see citing works
Comparison between new-generation SiPM-based and conventional PMT-based TOF-PET/CT
Wagatsuma, K., Miwa, K., Sakata, M., Oda, K., Ono, H., Kameyama, M., Toyohara, J., & Ishii, K. (2017) · Physica Medica · doi:10.1016/j.ejmp.2017.09.124 · Cited by 117 · see citing works
Lindström, E., Sundin, A., Trampal, C., Lindsjö, L., Ilan, E., Danfors, T., Antoni, G., Sörensen, J., & Lubberink, M. (2018) · Journal of Nuclear Medicine · doi:10.2967/jnumed.117.200790 · Cited by 99 · see citing works
Parvizi, N., Franklin, J. M., McGowan, D. R., Teoh, E., Bradley, K. M., & Gleeson, F. V. (2015) · European Journal of Radiology · doi:10.1016/j.ejrad.2015.06.025 · Cited by 91 · see citing works
Point-spread function reconstructed PET images of sub-centimeter lesions are not quantitative
Munk, O. L., Tolbod, L. P., Hansen, S. B., & Bogsrud, T. V. (2017) · EJNMMI Physics · doi:10.1186/s40658-016-0169-9 · Cited by 84 · see citing works
Howard, B. A., Morgan, R., Thorpe, M. P., Turkington, T. G., Oldan, J., James, O. G., & Borges-Neto, S. (2017) · Annals of Nuclear Medicine · doi:10.1007/s12149-017-1192-1 · Cited by 70 · see citing works
Sah, B.-R., Stolzmann, P., Delso, G., Wollenweber, S. D., Hüllner, M. W., Hakami, Y. A., Queiroz, M. A., Barbosa, F. D. G., von Schulthess, G. K., Pietsch, C., et al. (2016) · Nuclear Medicine Communications · doi:10.1097/mnm.0000000000000604 · Cited by 58 · see citing works
Giovacchini, G., Giovannini, E., Leoncini, R., Riondato, M., & Ciarmiello, A. (2017) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-017-3700-x · Cited by 51 · see citing works
Teoh, E., McGowan, D. R., Schuster, D. M., Tsakok, M., Gleeson, F. V., & Bradley, K. M. (2018) · British Journal of Radiology · doi:10.1259/bjr.20170727 · Cited by 42 · see citing works
Pegard, C., Gallazzini-Crepin, C., Giai, J., Dubreuil, J., Caoduro, C., Desruet, M.-D., Roux, J., Calizzano, A., Fagret, D., Lamesa, C., et al. (2014) · EJNMMI Research · doi:10.1186/s13550-014-0025-7 · Cited by 9 · see citing works
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Trägårdh, E., Enqvist, O., Ulén, J., Jögi, J., Bitzèn, U., Hedeer, F., Valind, K., Garpered, S., Hvittfeldt, E., Borrelli, P., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12092101 · Cited by 35 · see citing works
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Liberini, V., Pizzuto, D. A., Messerli, M., Orita, E., Grünig, H., Maurer, A. B., Mader, C. E., Husmann, L., Deandreis, D., Kotasidis, F. A., et al. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00570-y · Cited by 14 · see citing works
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Self-citationMiwa, K., Yamao, T., Hashimoto, F., Miyaji, N., Kamitaka, Y., Masubuchi, M., Murata, T., Yoshii, T., Kobayashi, R., Fukuda, S., et al. (2025) · Annals of Nuclear Medicine · doi:10.1007/s12149-025-02088-7 · Cited by 9 · see citing works
Sadeghi, F., Sheikhzadeh, P., Farzanehfar, S., Ghafarian, P., Moafpurian, Y., & Ay, M. (2023) · EJNMMI Physics · doi:10.1186/s40658-023-00587-y · Cited by 8 · see citing works
Bjöersdorff, M., Puterman, C., Oddstig, J., Amidi, J., Zackrisson, S., Kjölhede, H., Bjartell, A. S., Wollmer, P., & Trägårdh, E. (2022) · Clinical Physiology and Functional Imaging · doi:10.1111/cpf.12770 · Cited by 6 · see citing works
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Miwa, K., Miyaji, N., Yamao, T., Kamitaka, Y., Wagatsuma, K., & Murata, T. (2023) · Japanese Journal of Radiological Technology · doi:10.6009/jjrt.2023-2200 · Cited by 1 · see citing works
Murat, H., Zulkifli, M. A. A., Said, M. A., Kechik, M. M. A., Tahir, D., & Karim, M. K. A. (2024) · Radiography · doi:10.1016/j.radi.2024.12.011
Xu, L., Cui, C., Li, R., Yang, R., Liu, R., Meng, Q., & Wang, F. (2023) · Figshare · doi:10.6084/m9.figshare.22602265
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Leide-Svegborn, S., Minarik, D., Solem, R., Hägerström, D., & Andersson, M. (2025) · Radiation Protection Dosimetry · doi:10.1093/rpd/ncaf165
Self-citation
- Authors
- Bjöersdorff, M., Oddstig, J., Karindotter-Borgendahl, N., Almquist, H., Zackrisson, S., Minarik, D., & Trägårdh, E.
- Affiliations
- Lund University (SE)
- Corresponding author
- Bjöersdorff, Mimmi
- Venue
- EJNMMI Physics
- Published
- 2019-03-21
- Volume
- 6
- Issue
- 1
- Pages
- 5-5
- ISSN
- 2197-7364 (ISSN-L)
- PMC
- PMC6428870
- OpenAlex
- W2936746547
- Cited by
- 20
- References
- 18
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 3 · 2022: 6 · 2021: 2 · 2020: 4
- FWCI
- 1.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- EJNMMI Physics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- Knut och Alice Wallenbergs Stiftelse (-)
- 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
- 18F-fluorocholine, BSREM, prostate cancer, lymph node metastasis detection, OSEM, frame duration, contrast-to-noise ratio, image quality, standardized uptake value, low-dose imaging
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