Regularized reconstruction of digital time-of-flight 68Ga-PSMA-11 PET/CT for the detection of recurrent disease in prostate cancer patients
Open accessAbstract
Accurate localization of recurrent prostate cancer (PCa) is critical, especially if curative therapy is intended.With the aim to optimize target-to-background uptake ratio in 68 Ga-PSMA-11 PET, we investigated the image quality and quantitative measures of regularized reconstruction by block-sequential regularized expectation maximization (BSREM). Methods:The study encompassed retrospective reconstruction and analysis of 20 digital time-of-flight (TOF) PET/CT examinations acquired 60 min post injection of 2 MBq/kg of 68 Ga-PSMA-11 in PCa patients with biochemical relapse after primary treatment.Reconstruction by ordered-subsets expectation maximization (OSEM; 3 iterations, 16 subsets, 5 mm gaussian postprocessing filter) and BSREM (β-values of 100-1600) were used, both including TOF and point spread function (PSF) recovery.Background variability (BV) was measured by placing a spherical volume of interest in the right liver lobe and defined as the standard deviation divided by the mean standardized uptake value (SUV).The image quality was evaluated in terms of signal-to-noise ratio (SNR) and signal-to-background ratio (SBR), using SUVmax of the lesions.A visual assessment was performed by four observers.Results: OSEM reconstruction produced images with a BV of 15%, whereas BSREM with a β-value above 300 resulted in lower BVs than OSEM (36% with β 100, 8% with β 1300).Decreasing the acquisition duration from 2 to 1 and 0.5 min per bed position increased BV for both reconstruction methods, although BSREM with β-values equal to or higher than 800 and 1200, respectively, kept the BV below 15%.In comparison of BSREM with OSEM, the mean SNR improved by 25 to 66% with an increasing β-value in the range of 200-1300, whereas the mean SBR decreased with an increasing β-value, ranging from 0 to 125% with a β-value of 100 and 900, respectively.Decreased acquisition duration resulted in β-values of 800 to 1000 and 1200 to 1400 for 1 and 0.5 min per bed position, respectively, producing improved image quality measures compared with OSEM at a full acquisition duration of 2 min per bed position.The observer study showed a slight overall preference for BSREM β 900 although the interobserver variability was high.Conclusion: BSREM image reconstruction with β-values in the range of 400-900 resulted in lower BV and similar or improved SNR and SBR in comparison with OSEM.Key words: 68 Ga-PSMA-11, prostate cancer, PET/CT,
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
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?
In low-count conditions, the tendency to prefer smooth, low-noise images with beta values >600 over sharper images could become even more evident.
“In low-count conditions, this tendency to prefer smooth, low-noise images with beta values >600 over “sharper” images could become even more evident [112,170].”
From Influences on PET Quantification and Interpretation (2022) · cites this paper as [170]
The Results (page unavailable) report a slight overall preference for β 900, with high interobserver variability, and show that higher β values reduced noise during shorter acquisitions. The paper does not report observer preferences under those shorter, lower-count conditions, so it does not establish that the preference would become more evident. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSeveral authors have reported improved SNR and image quality with PL reconstruction for different radiopharmaceuticals, including [18F]FDG, [18F]F-PSMA-1007, [68Ga]Ga-PSMA-11, [68Ga]Ga-DOTATATE, and 89Zr-labelled tracers.
“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 [170]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Prostate-specific membrane antigen expression as a predictor of prostate cancer progression
Perner, S., Hofer, M. D., Kim, R., Shah, R. B., Li, H., Mӧller, P., Hautmann, R. E., Gschwend, J. E., Kuefer, R., & Rubin, M. A. (2007) · Human Pathology · doi:10.1016/j.humpath.2006.11.012 · Cited by 455 · see citing works
ROACH, P. J., Francis, R. J., Emmett, L., Hsiao, E., Kneebone, A. B., Hruby, G., Eade, T., Nguyen, Q. A., Thompson, B. D., Cusick, T., et al. (2017) · Journal of Nuclear Medicine · doi:10.2967/jnumed.117.197160 · Cited by 368 · see citing works
Calais, J., Czernin, J. G., Cao, M., Kishan, A. U., Hegde, J. V., Shaverdian, N., Sandler, K. A., Chu, F., King, C. R., Steinberg, M. L., et al. (2017) · Journal of Nuclear Medicine · doi:10.2967/jnumed.117.201749 · Cited by 280 · 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
Self-citationBoellaard, R., van Lingen, A., & Lammertsma, A. A. (2001) · PubMed · Cited by 195 · 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
Normal distribution pattern and physiological variants of 68Ga-PSMA-11 PET/CT imaging
Demirci, E., Şahin, O. E., Ocak, M., Akovalı, B., Nematyazar, J., & Kabasakal, L. (2016) · Nuclear Medicine Communications · doi:10.1097/mnm.0000000000000566 · Cited by 167 · see citing works
PSMA as a target for radiolabelled small molecules
Eder, M., Eisenhut, M., Babich, J. W., & Haberkorn, U. (2013) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-013-2374-2 · Cited by 149 · see citing works
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 · 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
Self-citationLiu, C., Liu, T., Zhang, N., Liu, Y., Li, N., Du, P., Yang, Y., Liu, M., Gong, K., Yang, X., et al. (2018) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-018-4037-9 · Cited by 79 · see citing works
Janssen, J.-C., Meißner, S., Woythal, N., Prasad, V., Brenner, W., Diederichs, G., Hamm, B., & Makowski, M. R. (2017) · European Radiology · doi:10.1007/s00330-017-4994-6 · Cited by 72 · 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
Uprimny, C., Svirydenka, A., Fritz, J., Kroiss, A. S., Nilica, B., Decristoforo, C., Haubner, R., von Guggenberg, E., Buxbaum, S., Horninger, W., et al. (2018) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-018-4048-6 · Cited by 50 · see citing works
ter Voert, E. E. G. W., Muehlematter, U. J., Delso, G., Pizzuto, D. A., Müller, J., Nagel, H. W., & Burger, I. A. (2018) · EJNMMI Research · doi:10.1186/s13550-018-0414-4 · Cited by 46 · see citing works
Alonso, Ó., dos Santos, G., Fontes, M. G., Balter, H. S., & Engler, H. (2018) · European Journal of Hybrid Imaging · doi:10.1186/s41824-018-0027-1 · Cited by 26 · see citing works
Nuyts, J. L., Beque, D., Dupont, P. J., & Mortelmans, L. (2005) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.2000.950051 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [6]Frenzel T Tienken M Abel M Berliner C Klutmann S Beyersdorff D The impact of [68Ga]PSMA I&T PET/CT on radiotherapy planning in patients with prostate cancer Strahlenther Onkol 2018 194 646 54 29572670
Cited by
Sorted by citations
Influences on PET Quantification and Interpretation
Rogasch, J. M. M., Hofheinz, F., van Heek, L., Voltin, C.-A., Boellaard, R., & Kobe, C. (2022) · Diagnostics · doi:10.3390/diagnostics12020451 · Cited by 50 · see citing works
Uprimny, C., Svirydenka, A., Fritz, J., Kroiss, A. S., Nilica, B., Decristoforo, C., Haubner, R., von Guggenberg, E., Buxbaum, S., Horninger, W., et al. (2018) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-018-4048-6 · Cited by 50 · see citing works
Targeted Radionuclide Therapy in Patient-Derived Xenografts Using 177Lu-EB-RGD
Zhao, L., Chen, H., Guo, Z., Fu, K., Yao, L., Fu, L., Guo, W., Wen, X., Jacobson, O., Zhang, X., et al. (2020) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-19-1098 · Cited by 36 · see citing works
Lindström, E., Lindsjö, L., Sundin, A., Sørensen, J. C. H., & Lubberink, M. (2020) · EJNMMI Physics · doi:10.1186/s40658-020-00310-1 · Cited by 35 · see citing works
Self-citationTrägårdh, E., Minarik, D., Brolin, G., Bitzén, U., Olsson, B., & Oddstig, J. (2020) · EJNMMI Physics · doi:10.1186/s40658-020-00298-8 · Cited by 27 · see citing works
Lindström, E., Oddstig, J., Danfors, T., Jögi, J., Hansson, O., & Lubberink, M. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102386 · Cited by 26 · see citing works
Self-citationYang, F., Ai, S., Wu, R., Lv, Y., Xie, H.-F., Dong, Y., Meng, Q., & Feng, W. (2021) · British Journal of Radiology · doi:10.1259/bjr.20201356 · Cited by 22 · see citing works
Positron Range Corrections and Denoising Techniques for Gallium-68 PET Imaging: A Literature Review
Gavriilidis, P., Koole, M., Annunziata, S., Mottaghy, F., & Wierts, R. (2022) · Diagnostics · doi:10.3390/diagnostics12102335 · Cited by 24 · see citing works
Moving the goalposts while scoring―the dilemma posed by new PET technologies
Rogasch, J. M. M., Boellaard, R., Pike, L., Borchmann, P., Johnson, P., Wolf, J., Barrington, S. F., & Kobe, C. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05403-2 · Cited by 20 · see citing works
Liberini, V., Kotasidis, F. A., Treyer, V., Messerli, M., Orita, E., Engel-Bicik, I., Siebenhüner, A. R., & Huellner, M. W. (2021) · Scientific Reports · doi:10.1038/s41598-020-80855-4 · Cited by 19 · see citing works
BSREM for Brain Metastasis Detection with 18F-FDG-PET/CT in Lung Cancer Patients
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
Comparison of 68Ga-PSMA-11 PET/CT with 11C-acetate PET/CT in re-staging of prostate cancer relapse
Regula, N. K., Kostaras, V., Johansson, S., Trampal, C., Lindström, E., Lubberink, M., Velikyan, I., & Sørensen, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-61910-6 · Cited by 14 · see citing works
Self-citationXu, L., Cui, C., Li, R., Yang, R., Liu, R., Meng, Q., & Wang, F. (2022) · EJNMMI Research · doi:10.1186/s13550-022-00945-4 · Cited by 12 · see citing works
Regula, N. K., Kostaras, V., Johansson, S., Trampal, C., Lindström, E., Lubberink, M., Iyer, V. V., Velikyan, I., & Sørensen, J. C. H. (2022) · European Journal of Hybrid Imaging · doi:10.1186/s41824-022-00127-4 · Cited by 11 · see citing works
Self-citationPost-reconstruction enhancement of [18F]FDG PET images with a convolutional neural network
Ly, J., Minarik, D., Jögi, J., Wollmer, P., & Trägårdh, E. (2021) · EJNMMI Research · doi:10.1186/s13550-021-00788-5 · Cited by 11 · see citing works
Beinecke, J. M., Anders, P., Schurrat, T., Heider, D., Luster, M., Librizzi, D., & Hauschild, A.-C. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105263 · Cited by 11 · see citing works
Santoro, M., Gala, G. D., Paolani, G., Zagni, F., Civollani, S., Strolin, S., & Strigari, L. (2023) · Physica Medica · doi:10.1016/j.ejmp.2022.102523 · Cited by 10 · see citing works
Sadeghi, F., Sheikhzadeh, P., Kasraie, N., Farzanehfar, S., Abbasi, M., Salehi, Y., & Ay, M. (2023) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-023-01299-4 · Cited by 9 · see citing works
Guo, S., Ren, J., Meng, Q., Zhang, B., Jiao, J., Han, D., Wu, P., Ma, S., Zhang, J., Xing, N., et al. (2024) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-024-06916-2 · Cited by 7 · see citing works
Jonmarker, O., Axelsson, R., Nilsson, T., & Gabrielson, S. (2021) · Diagnostics · doi:10.3390/diagnostics11040630 · Cited by 7 · see citing works
Rovera, G., Urso, L., Stracuzzi, F., Laudicella, R., Frantellizzi, V., Cottignoli, C., Gazzilli, M., Guglielmo, P., Panareo, S., Evangelista, L., et al. (2024) · Clinical and Translational Imaging · doi:10.1007/s40336-024-00650-3 · Cited by 5 · see citing works
Liu, Y., & Tang, S. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/5823720 · Cited by 5 · see citing works
RetractedCox, C. P. W., Brabander, T., Vegt, E., de Lussanet, Q. G., Graven, L. H., Verburg, F. A., & Segbers, M. (2024) · EJNMMI Physics · doi:10.1186/s40658-024-00629-z · Cited by 3 · see citing works
Rovera, G., Urso, L., Stracuzzi, F., Laudicella, R., Frantellizzi, V., Cottignoli, C., Gazzilli, M., Guglielmo, P., Panareo, S., Evangelista, L., et al. (2024) · Clinical and Translational Imaging · doi:10.1007/s40336-024-00653-0 · Cited by 3 · see citing works
Rovera, G., Grimaldi, S., Dall’Armellina, S., Zotta, M., Finessi, M., Passera, R., & Deandreis, D. (2023) · Diagnostics · doi:10.3390/diagnostics13233535 · Cited by 2 · see citing works
Digitale SPECT und PET: Klinische Konsequenzen
Rogasch, J. M. M. (2022) · Angewandte Nuklearmedizin · doi:10.1055/a-1715-5305 · Cited by 1 · see citing works
Digital PET for recurrent prostate cancer: how the technology help
Ricci, M., Carabellese, B., Pietroniro, D., Fojaja, M. R. G., Vincentis, G. D., & Cimini, A. (2023) · Clinical and Translational Imaging · doi:10.1007/s40336-023-00545-9
Digitale SPECT und PET: Klinische Konsequenzen
Rogasch, J. M. M. (2023) · Radiopraxis · doi:10.1055/a-1993-7420
Xu, L., Cui, C., Li, R., Yang, R., Liu, R., Meng, Q., & Wang, F. (2023) · Figshare · doi:10.6084/m9.figshare.22602265
Lindström, E., Lindsjö, L., Sundin, A., Sörensen, J., & Lubberink, M. (2020) · Figshare · doi:10.6084/m9.figshare.12487391
Self-citationLi, T., Ma, J., Wang, Y., & Li, F. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1713930
Pugliesi, R. A., Cannella, R., Mansour, K. B., Biagio, D. D., & Alongi, P. (2026) · Medicina · doi:10.3390/medicina62030610
- Authors
- Lindström, E., Velikyan, I., Regula, N., Alhuseinalkhudhur, A., Sundin, A., Sörensen, J., & Lubberink, M.
- Affiliations
- Uppsala University (SE)
- Corresponding author
- Lindström, Elin
- ORCID
- Venue
- Theranostics
- Publisher
- Ivyspring International Publisher
- Published
- 2019
- Volume
- 9
- Issue
- 12
- Pages
- 3476-3484
- ISSN
- 1838-7640 (ISSN-L)
- PMC
- PMC6587171
- OpenAlex
- W2951519500
- Cited by
- 32
- References
- 17
- Citations by year
- 2026: 2 · 2024: 4 · 2023: 6 · 2022: 8 · 2021: 5 · 2020: 6 · 2018: 1
- FWCI
- 3.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Theranostics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
- Funders
- Cancerfonden (2016/835)
- Akademiska Sjukhuset
- Uppsala Universitet
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Prostate Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiopharmaceutical Chemistry and Applications
- Keywords
- 68Ga-PSMA-11, prostate cancer, recurrent disease detection, OSEM, image reconstruction, background variation, signal-to-background ratio, signal-to-noise ratio, acquisition time, point spread function, biochemical recurrence, PSMA PET imaging, SUVmax
- MeSH terms
- Positron Emission Tomography Computed Tomography, Aged, Gallium Isotopes, Gallium Radioisotopes, Humans, Image Processing, Computer-Assisted, Male, Membrane Glycoproteins, Middle Aged, Neoplasm Recurrence, Local, Organometallic Compounds, Prostatic Neoplasms, Retrospective Studies, Radiopharmaceuticals
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