Detection of sub-centimeter lesions using digital TOF-PET/CT system combined with Bayesian penalized likelihood reconstruction algorithm



Many advances in PET/CT technology can potentially improve image quality and the ability to detect small lesions. A new digital TOF-PET/CT scanner based on silicon photomultipliers (SiPM) integrated with a Bayesian penalized likelihood (BPL) PET reconstruction algorithm (Q.Clear; GE Healthcare) has been introduced into clinical practice. The present study aimed to quantify the ability of a digital TOF-PET/CT scanner combined with BPL reconstruction to detect small lesions, and to determine the optimal penalization factor (β) in BPL to accurately detect such lesions.


All PET data were acquired from a NEMA body phantom using a Discovery MI (DMI) PET/CT system (GE Healthcare). The phantom included six spheres with diameters of 4, 5, 6, 8, 10, and 13 mm, and contained a background activity level of 5.3 kBq/mL, with target-to-background ratios (TBR) of 4:1 and 8:1. Images were reconstructed using a baseline OSEM algorithm, with OSEM + PSF, OSEM + TOF, OSEM + PSF + TOF, and BPL + PSF + TOF (β: 50–400). The matrix size was 192 × 192 and 384 × 384. Data acquired in 100-min list mode were re-binned into acquisition times ranging from 2 to 100 min. The quantitative accuracy and detectability of small hot spheres were evaluated by physical assessment of a recovery coefficient (RC) and a detectability index (DI), as well as visual assessment of PET images at each acquisition time.


The RC and DI of sub-centimeter spheres were improved, because the digital TOF-PET/CT scanner has a larger TOF performance gain due to better timing resolution. The RC and DI were higher with BPL in sub-centimeter spheres, than with other OSEM-based types of reconstruction. The BPL for an 8-mm sphere overestimated uptake due to edge artifact overshoot induced by PSF modeling. The variability of RC and DI for acquisition times and TBR differed considerably according to β values. The RC for ~ 8-mm spheres were > 1 at β values between 50 and 100, but were close to 1 at β value of 200. The visual scores for β = 200 in BPL were maximal, whereas those for spheres that were ≥ 6 mm exceeded the criterion of 3.


The BPL in the digital TOF-PET/CT scanner improved the quantitation and detectability of sub-centimeter spheres compared with OSEM-based reconstruction. Optimization of the β value in BPL might allow the detection of lesions ≤ 6 mm, although detectability depended on the TBR of lesions. A β value of 200 seemed optimal for detecting sub-centimeter lesions.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. 1.

    Townsend DW. Dual-modality imaging: combining anatomy and function. J Nucl Med. 2008;49:938–55.

    Article  Google Scholar 

  2. 2.

    Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54.

    CAS  Article  Google Scholar 

  3. 3.

    Soret M, Bacharach SL, Buvat I. Partial-volume effect in PET tumor imaging. J Nucl Med. 2007;48:932–45.

    Article  Google Scholar 

  4. 4.

    Fukukita H, Suzuki K, Matsumoto K, Terauchi T, Daisaki H, Ikari Y, et al. Japanese guideline for the oncology FDG-PET/CT data acquisition protocol: synopsis of version 2.0. Ann Nucl Med. 2014;28:693–705.

    Article  Google Scholar 

  5. 5.

    van der Vos CS, Koopman D, Rijnsdorp S, Arends AJ, Boellaard R, van Dalen JA, et al. Quantification, improvement, and harmonization of small lesion detection with state-of-the-art PET. Eur J Nucl Med Mol Imaging. 2017;44:4–16.

    Article  Google Scholar 

  6. 6.

    Daube-Witherspoon ME, Surti S, Perkins AE, Karp JS. Determination of accuracy and precision of lesion uptake measurements in human subjects with time-of-flight PET. J Nucl Med. 2014;55:602–7.

    Article  Google Scholar 

  7. 7.

    Bellevre D, Blanc Fournier C, Switsers O, Dugue AE, Levy C, Allouache D, et al. Staging the axilla in breast cancer patients with (1)(8)F-FDG PET: how small are the metastases that we can detect with new generation clinical PET systems? Eur J Nucl Med Mol Imaging. 2014;41:1103–12.

    CAS  Article  Google Scholar 

  8. 8.

    Fakhri G, Surti S, Trott CM, Scheuermann J, Karp JS. Improvement in lesion detection with whole-body oncologic time-of-flight pet. J Nucl Med. 2011;52:347–53.

    Article  Google Scholar 

  9. 9.

    Murata T, Miwa K, Miyaji N, Wagatsuma K, Hasegawa T, Oda K, et al. Evaluation of spatial dependence of point spread function-based PET reconstruction using a traceable point-like 22Na source. EJNMMI Phys. 2016;3:26.

    Article  Google Scholar 

  10. 10.

    Hashimoto N, Morita K, Tsutsui Y, Himuro K, Baba S, Sasaki M. Time-of-flight information improved the detectability of subcentimeter spheres using a clinical PET/CT scanner. J Nucl Med Technol. 2018;46:268–73.

    Article  Google Scholar 

  11. 11.

    Munk OL, Tolbod LP, Hansen SB, Bogsrud TV. Point-spread function reconstructed PET images of sub-centimeter lesions are not quantitative. EJNMMI Phys. 2017;4:5.

    CAS  Article  Google Scholar 

  12. 12.

    Ahn S, Ross SG, Asma E, Miao J, Jin X, Cheng L, et al. Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET. Phys Med Biol. 2015;60:5733–51.

    Article  Google Scholar 

  13. 13.

    Teoh EJ, McGowan DR, Macpherson RE, Bradley KM, Gleeson FV. Phantom and clinical evaluation of the Bayesian penalized likelihood reconstruction algorithm Q.Clear on an LYSO PET/CT system. J Nucl Med. 2015;56:1447–522.

    CAS  Article  Google Scholar 

  14. 14.

    Ahn S, Fessler JA. Globally convergent image reconstruction for emission tomography using relaxed ordered subsets algorithms. IEEE Trans Med Imaging. 2003;22:613–26.

    Article  Google Scholar 

  15. 15.

    Gnesin S, Kieffer C, Zeimpekis K, Papazyan JP, Guignard R, Prior JO, et al. Phantom-based image quality assessment of clinical (18)F-FDG protocols in digital PET/CT and comparison to conventional PMT-based PET/CT. EJNMMI Phys. 2020;7:1.

    Article  Google Scholar 

  16. 16.

    Howard BA, Morgan R, Thorpe MP, Turkington TG, Oldan J, James OG, et al. Comparison of Bayesian penalized likelihood reconstruction versus OS-EM for characterization of small pulmonary nodules in oncologic PET/CT. Ann Nucl Med. 2017;31:623–8.

    CAS  Article  Google Scholar 

  17. 17.

    Teoh EJ, McGowan DR, Bradley KM, Belcher E, Black E, Gleeson FV. Novel penalised likelihood reconstruction of PET in the assessment of histologically verified small pulmonary nodules. Eur Radiol. 2016;26:576–84.

    Article  Google Scholar 

  18. 18.

    Schwyzer M, Martini K, Benz DC, Burger IA, Ferraro DA, Kudura K, et al. Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance. Eur Radiol. 2019;30:2031–40.

    Article  Google Scholar 

  19. 19.

    Rogasch JM, Suleiman S, Hofheinz F, Bluemel S, Lukas M, Amthauer H, et al. Reconstructed spatial resolution and contrast recovery with Bayesian penalized likelihood reconstruction (Q.Clear) for FDG-PET compared to time-of-flight (TOF) with point spread function (PSF). EJNMMI Phys. 2020;7:2.

    Article  Google Scholar 

  20. 20.

    Wagatsuma K, Miwa K, Sakata M, Oda K, Ono H, Kameyama M, et al. Comparison between new-generation SiPM-based and conventional PMT-based TOF-PET/CT. Phys Med. 2017;42:203–10.

    Article  Google Scholar 

  21. 21.

    Hsu DFC, Ilan E, Peterson WT, Uribe J, Lubberink M, Levin CS. Studies of a next-generation silicon-photomultiplier-based time-of-flight PET/CT system. J Nucl Med. 2017;58:1511–8.

    CAS  Article  Google Scholar 

  22. 22.

    Sonni I, Baratto L, Park S, Hatami N, Srinivas S, Davidzon G, et al. Initial experience with a SiPM-based PET/CT scanner: influence of acquisition time on image quality. EJNMMI Phys. 2018;5:9.

    Article  Google Scholar 

  23. 23.

    Baratto L, Park SY, Hatami N, Davidzon G, Srinivas S, Gambhir SS, et al. 18F-FDG silicon photomultiplier PET/CT: A pilot study comparing semi-quantitative measurements with standard PET/CT. PLoS ONE. 2017;12:e0178936.

    Article  Google Scholar 

  24. 24.

    Lindstrom E, Sundin A, Trampal C, Lindsjo L, Ilan E, Danfors T, et al. Evaluation of penalized-likelihood estimation reconstruction on a digital time-of-flight PET/CT scanner for (18)F-FDG whole-body examinations. J Nucl Med. 2018;59:1152–8.

    Article  Google Scholar 

  25. 25.

    Aljared A, Alharbi AA, Huellner MW. BSREM reconstruction for improved detection of in-transit metastases with digital FDG-PET/CT in patients with malignant melanoma. Clin Nucl Med. 2018;43:370–1.

    Article  Google Scholar 

  26. 26.

    Sampaio Vieira T, Borges Faria D, Azevedo Silva F, Barroso S, Fonseca G, Pereira OJ. The impact of a Bayesian penalized-likelihood reconstruction algorithm on delayed-time-point Ga-68-PSMA PET for improved recurrent prostate cancer detection. Eur J Nucl Med Mol Imaging. 2018;45:1461–2.

    CAS  Article  Google Scholar 

  27. 27.

    Wangerin KA, Ahn S, Wollenweber S, Ross SG, Kinahan PE, Manjeshwar RM. Evaluation of lesion detectability in positron emission tomography when using a convergent penalized likelihood image reconstruction method. J Med Imaging (Bellingham). 2017;4:011002.

    Article  Google Scholar 

  28. 28.

    Yamaguchi S, Wagatsuma K, Miwa K, Ishii K, Inoue K, Fukushi M. Bayesian penalized-likelihood reconstruction algorithm suppresses edge artifacts in PET reconstruction based on point-spread-function. Phys Med. 2018;47:73–9.

    Article  Google Scholar 

  29. 29.

    Adler S, Seidel J, Choyke P, Knopp MV, Binzel K, Zhang J, et al. Minimum lesion detectability as a measure of PET system performance. EJNMMI Phys. 2017;4:13.

    Article  Google Scholar 

  30. 30.

    Kidera D, Kihara K, Akamatsu G, Mikasa S, Taniguchi T, Tsutsui Y, et al. The edge artifact in the point-spread function-based PET reconstruction at different sphere-to-background ratios of radioactivity. Ann Nucl Med. 2016;30:97–103.

    CAS  Article  Google Scholar 

  31. 31.

    Rowley LM, Bradley KM, Boardman P, Hallam A, McGowan DR. Optimization of image reconstruction for (90)Y selective internal radiotherapy on a lutetium yttrium orthosilicate PET/CT system using a bayesian penalized likelihood reconstruction algorithm. J Nucl Med. 2017;58:658–64.

    CAS  Article  Google Scholar 

  32. 32.

    Rogasch JM, Hofheinz F, Lougovski A, Furth C, Ruf J, Grosser OS, et al. The influence of different signal-to-background ratios on spatial resolution and F18-FDG-PET quantification using point spread function and time-of-flight reconstruction. EJNMMI Phys. 2014;1:12.

    Article  Google Scholar 

  33. 33.

    Rogasch JM, Steffen IG, Hofheinz F, Grosser OS, Furth C, Mohnike K, et al. The association of tumor-to-background ratios and SUVmax deviations related to point spread function and time-of-flight F18-FDG-PET/CT reconstruction in colorectal liver metastases. EJNMMI Res. 2015;5:31.

    Article  Google Scholar 

  34. 34.

    Koopman D, van Dalen JA, Lagerweij MC, Arkies H, de Boer J, Oostdijk AH, et al. Improving the detection of small lesions using a state-of-the-art time-of-flight PET/CT system and small-voxel reconstructions. J Nucl Med Technol. 2015;43:21–7.

    Article  Google Scholar 

  35. 35.

    Morey AM, Noo F, Kadrmas DJ. Effect of using 2 mm voxels on observer performance for PET lesion detection. IEEE Trans Nucl Sci. 2016;63:1359–66.

    CAS  Article  Google Scholar 

  36. 36.

    te Riet J, Rijnsdorp S, Roef MJ, Arends AJ. Evaluation of a Bayesian penalized likelihood reconstruction algorithm for low-count clinical 18F-FDG PET/CT. EJNMMI Phys. 2019;6:32.

    Article  Google Scholar 

  37. 37.

    Texte E, Gouel P, Thureau S, Lequesne J, Barres B, Edet-Sanson A, et al. Impact of the Bayesian penalized likelihood algorithm (Q.Clear(R)) in comparison with the OSEM reconstruction on low contrast PET hypoxic images. EJNMMI Phys. 2020;7:28.

    Article  Google Scholar 

  38. 38.

    Reynes-Llompart G, Gamez-Cenzano C, Vercher-Conejero JL, Sabate-Llobera A, Calvo-Malvar N, Marti-Climent JM. Phantom, clinical, and texture indices evaluation and optimization of a penalized-likelihood image reconstruction method (Q.Clear) on a BGO PET/CT scanner. Med Phys. 2018;45:3214–22.

    Article  Google Scholar 

  39. 39.

    O’ Doherty J, McGowan DR, Abreu C, Barrington S. Effect of Bayesian-penalized likelihood reconstruction on [13N]-NH3 rest perfusion quantification. J Nucl Cardiol. 2017;24:282–90.

    Article  Google Scholar 

Download references


For valuable contributions in the data collection and interpretation processes for this publication, we would like to thank Mr. Akira Hirayama and Mr. Hirofumi Kawakami from GE Healthcare and Dr. Hiroyuki Shinohara from Tokyo Metropolitan University. This work was supported in part by KAKENHI Grant-in-Aid for Young Scientists (B) (No. 16K19831) and from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), the Japanese Government, and an Academic Research Grant from International University of Health and Welfare.

Author information



Corresponding author

Correspondence to Kenta Miwa.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Miwa, K., Wagatsuma, K., Nemoto, R. et al. Detection of sub-centimeter lesions using digital TOF-PET/CT system combined with Bayesian penalized likelihood reconstruction algorithm. Ann Nucl Med 34, 762–771 (2020).

Download citation


  • Q.Clear
  • Detectability
  • Sub-centimeter lesion
  • SiPM
  • BPL
  • TOF