Skip to main content

Advertisement

SpringerLink
Log in

New standards for phantom image quality and SUV harmonization range for multicenter oncology PET studies

  • Special Article
  • Published:
Annals of Nuclear Medicine Aims and scope Submit manuscript

Abstract

Not only visual interpretation for lesion detection, staging, and characterization, but also quantitative treatment response assessment are key roles for 18F-FDG PET in oncology. In multicenter oncology PET studies, image quality standardization and SUV harmonization are essential to obtain reliable study outcomes. Standards for image quality and SUV harmonization range should be regularly updated according to progress in scanner performance. Accordingly, the first aim of this study was to propose new image quality reference levels to ensure small lesion detectability. The second aim was to propose a new SUV harmonization range and an image noise criterion to minimize the inter-scanner and intra-scanner SUV variabilities. We collected a total of 37 patterns of images from 23 recent PET/CT scanner models using the NEMA NU2 image quality phantom. PET images with various acquisition durations of 30–300 s and 1800 s were analyzed visually and quantitatively to derive visual detectability scores of the 10-mm-diameter hot sphere, noise-equivalent count (NECphantom), 10-mm sphere contrast (QH,10 mm), background variability (N10 mm), contrast-to-noise ratio (QH,10 mm/N10 mm), image noise level (CVBG), and SUVmax and SUVpeak for hot spheres (10–37 mm diameters). We calculated a reference level for each image quality metric, so that the 10-mm sphere can be visually detected. The SUV harmonization range and the image noise criterion were proposed with consideration of overshoot due to point-spread function (PSF) reconstruction. We proposed image quality reference levels as follows: QH,10 mm/N10 mm ≥ 2.5 and CVBG ≤ 14.1%. The 10th–90th percentiles in the SUV distributions were defined as the new SUV harmonization range. CVBG ≤ 10% was proposed as the image noise criterion, because the intra-scanner SUV variability significantly depended on CVBG. We proposed new image quality reference levels to ensure small lesion detectability. A new SUV harmonization range (in which PSF reconstruction is applicable) and the image noise criterion were also proposed for minimizing the SUV variabilities. Our proposed new standards will facilitate image quality standardization and SUV harmonization of multicenter oncology PET studies. The reliability of multicenter oncology PET studies will be improved by satisfying the new standards.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Rohren EM, Turkington TG, Coleman RE. Clinical applications of PET in oncology. Radiology. 2004;231:305–32.

    PubMed  Google Scholar 

  2. Delbeke D. Oncological applications of FDG PET imaging: brain tumors, colorectal cancer, lymphoma and melanoma. J Nucl Med. 1999;40:591–603.

    CAS  PubMed  Google Scholar 

  3. Fletcher JW, Djulbegovic B, Soares HP, Siegel BA, Lowe VJ, Lyman GH, et al. Recommendations on the use of 18F-FDG PET in oncology. J Nucl Med. 2008;49:480–508.

    PubMed  Google Scholar 

  4. Weber WA. Assessing tumor response to therapy. J Nucl Med. 2009;50:1S-10S.

    CAS  PubMed  Google Scholar 

  5. O’Connor JPB, Aboagye EO, Adams JE, Aerts HJWL, Barrington SF, Beer AJ, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14:169–86.

    PubMed  Google Scholar 

  6. Meikle SR, Sossi V, Roncali E, Cherry SR, Banati R, Mankoff D, et al. Quantitative PET in the 2020s: a roadmap. Phys Med Biol. 2021;66:06RM01.

    CAS  PubMed  Google Scholar 

  7. Young H, Baum R, Cremerius U, Herholz K, Hoekstra O, Lammertsma AA, et al. Measurement of clinical and subclinical tumour response using [18F]-fluorodeoxyglucose and positron emission tomography: review and 1999 EORTC recommendations. European Organization for Research and Treatment of Cancer (EORTC) PET Study Group. Eur J Cancer. 1999;35:1773–82.

    CAS  PubMed  Google Scholar 

  8. Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving Considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50(Suppl 1):122S-S150.

    CAS  PubMed  Google Scholar 

  9. Judson I, Scurr M, Gardner K, Barquin E, Marotti M, Collins B, et al. Phase II study of cediranib in patients with advanced gastrointestinal stromal tumors or soft-tissue sarcoma. Clin Cancer Res. 2014;20:3603–12.

    CAS  PubMed  Google Scholar 

  10. Yap TA, Arkenau H-T, Camidge DR, George S, Serkova NJ, Gwyther SJ, et al. First-in-human phase I trial of two schedules of OSI-930, a novel multikinase inhibitor, incorporating translational proof-of-mechanism studies. Clin Cancer Res. 2013;19:909–19.

    CAS  PubMed  Google Scholar 

  11. Connolly RM, Leal JP, Solnes L, Huang C-Y, Carpenter A, Gaffney K, et al. TBCRC026: phase II trial correlating standardized uptake value with pathologic complete response to pertuzumab and trastuzumab in breast cancer. J Clin Oncol. 2019;37:714–22.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Boellaard R. Standards for PET image acquisition and quantitative data analysis. J Nucl Med. 2009;50(Suppl 1):11S-20S.

    CAS  PubMed  Google Scholar 

  13. Adams MC, Turkington TG, Wilson JM, Wong TZ. A systematic review of the factors affecting accuracy of SUV measurements. AJR Am J Roentgenol. 2010;195:310–20.

    PubMed  Google Scholar 

  14. Fahey FH, Kinahan PE, Doot RK, Kocak M, Thurston H, Poussaint TY. Variability in PET quantitation within a multicenter consortium. Med Phys. 2010;37:3660–6.

    PubMed  PubMed Central  Google Scholar 

  15. Daisaki H, Kitajima K, Nakajo M, Watabe T, Ito K, Sakamoto F, et al. Usefulness of semi-automatic harmonization strategy of standardized uptake values for multicenter PET studies. Sci Rep. 2021;11:8517.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Kinahan PE, Doot RK, Wanner-Roybal M, Bidaut LM, Armato SG, Meyer CR, et al. PET/CT assessment of response to therapy: tumor change measurement, truth data, and error. Transl Oncol. 2009;2:223–30.

    PubMed  PubMed Central  Google Scholar 

  17. Doot RK, Kurland BF, Kinahan PE, Mankoff DA. Design considerations for using PET as a response measure in single site and multicenter clinical trials. Acad Radiol. 2012;19:184–90.

    PubMed  Google Scholar 

  18. FDG-PET/CT Technical Committee. FDG-PET/CT as an Imaging Biomarker Measuring Response to Cancer Therapy, Quantitative Imaging Biomarkers Alliance, Version 1.13, Technically Confirmed Version. QIBA, November 18, 2016. Available from: RSNA.ORG/QIBA.

  19. Graham MM, Wahl RL, Hoffman JM, Yap JT, Sunderland JJ, Boellaard R, et al. Summary of the UPICT protocol for 18F-FDG PET/CT imaging in oncology clinical trials. J Nucl Med. 2015;56:955–61.

    PubMed  Google Scholar 

  20. Scheuermann JS, Saffer JR, Karp JS, Levering AM, Siegel BA. Qualification of PET scanners for use in multicenter cancer clinical trials: the American College of Radiology Imaging Network experience. J Nucl Med. 2009;50:1187–93.

    PubMed  Google Scholar 

  21. Christian P. Use of a precision fillable clinical simulator phantom for PET/CT scanner validation in multi-center clinical trials: The SNM Clinical Trials Network (CTN) Program. J Nucl Med. 2012;53(Suppl 1):437.

    Google Scholar 

  22. Kaalep A, Sera T, Rijnsdorp S, Yaqub M, Talsma A, Lodge MA, et al. Feasibility of state of the art PET/CT systems performance harmonisation. Eur J Nucl Med Mol Imaging. 2018;45:1344–61.

    PubMed  PubMed Central  Google Scholar 

  23. Kaalep A, Sera T, Oyen W, Krause BJ, Chiti A, Liu Y, et al. EANM/EARL FDG-PET/CT accreditation—summary results from the first 200 accredited imaging systems. Eur J Nucl Med Mol Imaging. 2018;45:412–22.

    CAS  PubMed  Google Scholar 

  24. Kinahan PE, Perlman ES, Sunderland JJ, Subramaniam R, Wollenweber SD, Turkington TG, et al. The QIBA profile for FDG PET/CT as an imaging biomarker measuring response to cancer therapy. Radiology. 2020;294:647–57.

    PubMed  Google Scholar 

  25. Makris NE, Huisman MC, Kinahan PE, Lammertsma AA, Boellaard R. Evaluation of strategies towards harmonization of FDG PET/CT studies in multicentre trials: comparison of scanner validation phantoms and data analysis procedures. Eur J Nucl Med Mol Imaging. 2013;40:1507–15.

    PubMed  PubMed Central  Google Scholar 

  26. Sunderland JJ, Christian PE. Quantitative PET/CT scanner performance characterization based upon the society of nuclear medicine and molecular imaging clinical trials network oncology clinical simulator phantom. J Nucl Med. 2015;56:145–52.

    PubMed  Google Scholar 

  27. Japanese Society of Nuclear Medicine. Standard PET imaging protocols and phantom test procedures and criteria: executive summary. 2017. http://jsnm.sakura.ne.jp/wp_jsnm/wp-content/themes/theme_jsnm/doc/StandardPETProtocolPhantom20170201.pdf. Accessed 14 Aug 2021.

  28. Senda M. Standardization of PET imaging and site qualification program by JSNM: collaboration with EANM/EARL. Ann Nucl Med. 2020;34:873–4.

    PubMed  Google Scholar 

  29. 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.

    PubMed  PubMed Central  Google Scholar 

  30. Panin VY, Kehren F, Michel C, Casey M. Fully 3-D PET reconstruction with system matrix derived from point source measurements. IEEE Trans Med Imaging. 2006;25:907–21.

    PubMed  Google Scholar 

  31. Rahmim A, Qi J, Sossi V. Resolution modeling in PET imaging: theory, practice, benefits, and pitfalls. Med Phys. 2013;40:064301.

    PubMed  PubMed Central  Google Scholar 

  32. Vandenberghe S, Mikhaylova E, D’Hoe E, Mollet P, Karp JS. Recent developments in time-of-flight PET. EJNMMI Physics. 2016;3:3.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Surti S, Karp JS. Update on latest advances in time-of-flight PET. Phys Med. 2020;80:251–8.

    PubMed  PubMed Central  Google Scholar 

  34. 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.

    PubMed  Google Scholar 

  35. van Sluis J, Boellaard R, Somasundaram A, van Snick PH, Borra RJH, Dierckx RAJO, et al. Image quality and semiquantitative measurements on the Biograph Vision PET/CT System: Initial experiences and comparison with the Biograph mCT. J Nucl Med. 2020;61:129–35.

    PubMed  Google Scholar 

  36. Kataoka J, Kishimoto A, Fujita T, Nishiyama T, Kurei Y, Tsujikawa T, et al. Recent progress of MPPC-based scintillation detectors in high precision X-ray and gamma-ray imaging. Nucl Instruments Method Phys Res Sect A Accel Spectrometers, Detect Assoc Equip. 2015;784:248–54.

    CAS  Google Scholar 

  37. 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.

    PubMed  Google Scholar 

  38. Ota R. Photon counting detectors and their applications ranging from particle physics experiments to environmental radiation monitoring and medical imaging. Radiol Phys Technol. 2021;14:134–48.

    PubMed  Google Scholar 

  39. Boellaard R, Sera T, Kaalep A, Hoekstra OS, Barrington SF, Zijlstra JM. Updating PET/CT performance standards and PET/CT interpretation criteria should go hand in hand. EJNMMI Res. 2019;9:5–6.

    Google Scholar 

  40. Weber WA, Gatsonis CA, Mozley PD, Hanna LG, Shields AF, Aberle DR, et al. Repeatability of 18F-FDG PET/CT in advanced non-small cell lung cancer: prospective assessment in 2 multicenter trials. J Nucl Med. 2015;56:1137–43.

    CAS  PubMed  Google Scholar 

  41. Machtay M, Duan F, Siegel BA, Snyder BS, Gorelick JJ, Reddin JS, et al. Prediction of survival by [18F]fluorodeoxyglucose positron emission tomography in patients with locally advanced non-small-cell lung cancer undergoing definitive chemoradiation therapy: results of the ACRIN 6668/RTOG 0235 trial. J Clin Oncol. 2013;31:3823–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Kahraman D, Scheffler M, Zander T, Nogova L, Lammertsma AA, Boellaard R, et al. Quantitative analysis of response to treatment with erlotinib in advanced non-small cell lung cancer using 18F-FDG and 3’-deoxy-3’-18F-fluorothymidine PET. J Nucl Med. 2011;52:1871–7.

    CAS  PubMed  Google Scholar 

  43. Lodge MA, Chaudhry MA, Wahl RL. Noise considerations for PET quantification using maximum and peak standardized uptake value. J Nucl Med. 2012;53:1041–7.

    CAS  PubMed  Google Scholar 

  44. Akamatsu G, Ikari Y, Nishida H, Nishio T, Ohnishi A, Maebatake A, et al. Influence of statistical fluctuation on reproducibility and accuracy of SUVmax and SUVpeak: a phantom study. J Nucl Med Technol. 2015;43:222–6.

    PubMed  Google Scholar 

  45. Kaneta T, Ogawa M, Motomura N, Iizuka H, Arisawa T, Hino-Shishikura A, et al. Initial evaluation of the Celesteion large-bore PET/CT scanner in accordance with the NEMA NU2–2012 standard and the Japanese guideline for oncology FDG PET/CT data acquisition protocol version 2.0. EJNMMI Res. 2017;7:83.

    PubMed  PubMed Central  Google Scholar 

  46. Reddin JS, Scheuermann JS, Bharkhada D, Smith AM, Casey ME, Conti M, et al. Performance evaluation of the SiPM-based siemens biograph vision PET/CT system. IEEE Nucl Sci Symp Med Imaging Conf Rec. 2018;2018:1–5.

    Google Scholar 

  47. Rausch I, Cal-González J, Dapra D, Gallowitsch HJ, Lind P, Beyer T, et al. Performance evaluation of the Biograph mCT Flow PET/CT system according to the NEMA NU2-2012 standard. EJNMMI Phys. 2015;2:26.

    PubMed  PubMed Central  Google Scholar 

  48. Jakoby BW, Bercier Y, Conti M, Casey ME, Bendriem B, Townsend DW. Physical and clinical performance of the mCT time-of-flight PET/CT scanner. Phys Med Biol. 2011;56:2375–89.

    CAS  PubMed  Google Scholar 

  49. Pan T, Einstein SA, Kappadath SC, Grogg KS, Lois Gomez C, Alessio AM, et al. Performance evaluation of the 5-Ring GE Discovery MI PET/CT system using the national electrical manufacturers association NU 2–2012 Standard. Med Phys. 2019;46:3025–33.

    PubMed  Google Scholar 

  50. 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  PubMed  Google Scholar 

  51. Vandendriessche D, Uribe J, Bertin H, De Geeter F. Performance characteristics of silicon photomultiplier based 15-cm AFOV TOF PET/CT. EJNMMI Phys. 2019;6:8.

    PubMed  PubMed Central  Google Scholar 

  52. Michopoulou S, O’Shaughnessy E, Thomson K, Guy MJ. Discovery molecular imaging digital ready PET/CT performance evaluation according to the NEMA NU2-2012 standard. Nucl Med Commun. 2019;40:270–7.

    PubMed  Google Scholar 

  53. Reynés-Llompart G, Gámez-Cenzano C, Romero-Zayas I, Rodríguez-Bel L, Vercher-Conejero JL, Martí-Climent JM. Performance characteristics of the whole-body discovery IQ PET/CT system. J Nucl Med. 2017;58:1155–61.

    PubMed  Google Scholar 

  54. Demir M, Toklu T, Abuqbeitah M, Çetin H, Sezgin HS, Yeyin N, et al. Evaluation of PET scanner performance in PET/MR and PET/CT systems: NEMA tests. Mol Imaging Radionucl Ther. 2018;27:10–8.

    PubMed  PubMed Central  Google Scholar 

  55. Bettinardi V, Presotto L, Rapisarda E, Picchio M, Gianolli L, Gilardi MC. Physical performance of the new hybrid PET∕CT Discovery-690. Med Phys. 2011;38:5394–411.

    CAS  PubMed  Google Scholar 

  56. De Ponti E, Morzenti S, Guerra L, Pasquali C, Arosio M, Bettinardi V, et al. Performance measurements for the PET/CT Discovery-600 using NEMA NU 2–2007 standards. Med Phys. 2011;38:968–74.

    PubMed  Google Scholar 

  57. Zhang J, Maniawski P, Knopp MV. Performance evaluation of the next generation solid-state digital photon counting PET/CT system. EJNMMI Res. 2018. https://doi.org/10.1186/s13550-018-0448-7.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Kolthammer JA, Su K, Grover A, Narayanan M, Jordan DW, Muzic RF. Performance evaluation of the Ingenuity TF PET/CT scanner with a focus on high count-rate conditions. Phys Med Biol. 2014;59:3843–59.

    PubMed  PubMed Central  Google Scholar 

  59. Surti S, Kuhn A, Werner ME, Perkins AE, Kolthammer J, Karp JS. Performance of Philips Gemini TF PET/CT scanner with special consideration for its time-of-flight imaging capabilities. J Nucl Med. 2007;48:471–80.

    PubMed  Google Scholar 

  60. Xu B, Changbin L, Yun D, Renming T, Yachao L, Hui Y, et al. Performance evaluation of a high-resolution TOF clinical PET/CT. J Nucl Med. 2016;57(Suppl 2):202.

    Google Scholar 

  61. 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;56:1447–52.

    CAS  PubMed  Google Scholar 

  62. Matsumoto K, Endo K. Development of analysis software package for the two kinds of Japanese Fluoro-D-glucose-positron emission tomography guideline. Japanese J Radiol Technol. 2013;69:648–54.

    Google Scholar 

  63. NEMA. NEMA Standards Publication NU 2–2018: performance measurements of positron emission tomographs. Rosslyn, VA: National Electrical Manufacturers Association; 2018.

  64. Strother SC, Casey ME, Hoffman EJ. Measuring PET scanner sensitivity: relating countrates to image signal-to-noise ratios using noise equivalents counts. IEEE Trans Nucl Sci. 1990;37:783–8.

    Google Scholar 

  65. Badawi RD, Dahlbom M. NEC: some coincidences are more equivalent than others. J Nucl Med. 2005;46:1767–8.

    PubMed  Google Scholar 

  66. Brasse D, Kinahan PE, Lartizien C, Comtat C, Casey M, Michel C. Correction methods for random coincidences in fully 3D whole-body PET: impact on data and image quality. J Nucl Med. 2005;46:859–67.

    PubMed  Google Scholar 

  67. Reader AJ, Julyan PJ, Williams H, Hastings DL, Zweit J. EM algorithm resolution modeling by image-space convolution for PET reconstruction. IEEE Nucl Sci Symp Conf Rec. 2002;2002:1221–5.

    Google Scholar 

  68. 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  PubMed  Google Scholar 

  69. Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant. 2013;48:452–8.

    CAS  PubMed  Google Scholar 

  70. Masuda Y, Kondo C, Matsuo Y, Uetani M, Kusakabe K. Comparison of imaging protocols for 18F-FDG PET/CT in overweight patients: optimizing scan duration versus administered dose. J Nucl Med. 2009;50:844–8.

    PubMed  Google Scholar 

  71. Chang T, Chang G, Clark JW, Diab RH, Rohren E, Mawlawi OR. Reliability of predicting image signal-to-noise ratio using noise equivalent count rate in PET imaging. Med Phys. 2012;39:5891–900.

    PubMed  PubMed Central  Google Scholar 

  72. Maebatake A, Akamatsu G, Miwa K, Tsutsui Y, Himuro K, Baba S, et al. Relationship between the image quality and noise-equivalent count in time-of-flight positron emission tomography. Ann Nucl Med. 2016;30:68–74.

    PubMed  Google Scholar 

  73. Akamatsu G, Ishikawa K, Mitsumoto K, Taniguchi T, Ohya N, Baba S, et al. Improvement in PET/CT image quality with a combination of point-spread function and time-of-flight in relation to reconstruction parameters. J Nucl Med. 2012;53:1716–22.

    PubMed  Google Scholar 

  74. Matsumoto K, Suzuki K, Fukukita H, Ikari Y, Oda K, Kimura Y, et al. Variability in PET quantitation within a multicenter studies in Japan. Eur J Nucl Med Mol Imaging. 2013;40(Suppl 2):S305.

    Google Scholar 

  75. El 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.

    PubMed  Google Scholar 

  76. Akamatsu G, Mitsumoto K, Taniguchi T, Tsutsui Y, Baba S, Sasaki M. Influences of point-spread function and time-of-flight reconstructions on standardized uptake value of lymph node metastases in FDG-PET. Eur J Radiol. 2014;83:226–30.

    PubMed  Google Scholar 

  77. 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  PubMed  PubMed Central  Google Scholar 

  78. Kaalep A, Burggraaff CN, Pieplenbosch S, Verwer EE, Sera T, Zijlstra J, et al. Quantitative implications of the updated EARL 2019 PET–CT performance standards. EJNMMI Phys. 2019;6:1–16.

    Google Scholar 

  79. 18F-FDG PET/CT UPICT Protocol Writing Committee. UPICT Oncology FDG-PET CT Protocol. http://qibawiki.rsna.org/images/d/de/UPICT_Oncologic_FDG-PETCTProtocol_6-07-13.pdf.

  80. Ulrich EJ, Sunderland JJ, Smith BJ, Mohiuddin I, Parkhurst J, Plichta KA, et al. Automated model-based quantitative analysis of phantoms with spherical inserts in FDG PET scans. Med Phys. 2018;45:258–76.

    CAS  PubMed  Google Scholar 

  81. SNMMI Phantom Analysis Toolkit (PAT). https://www.snmmi.org/PAT. Accessed 14 Aug 2021.

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

    PubMed  Google Scholar 

  83. Vanderhoek M, Perlman SB, Jeraj R. Impact of the definition of peak standardized uptake value on quantification of treatment response. J Nucl Med. 2012;53:4–11.

    CAS  PubMed  Google Scholar 

  84. Watabe T, Tatsumi M, Watabe H, Isohashi K, Kato H, Yanagawa M, et al. Intratumoral heterogeneity of F-18 FDG uptake differentiates between gastrointestinal stromal tumors and abdominal malignant lymphomas on PET/CT. Ann Nucl Med. 2012;26:222–7.

    PubMed  Google Scholar 

  85. Miwa K, Inubushi M, Wagatsuma K, Nagao M, Murata T, Koyama M, et al. FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules. Eur J Radiol. 2014;83:715–9.

    PubMed  Google Scholar 

  86. Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJR. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2013;40:133–40.

    PubMed  Google Scholar 

  87. Lim R, Eaton A, Lee NY, Setton J, Ohri N, Rao S, et al. 18F-FDG PET/CT metabolic tumor volume and total lesion glycolysis predict outcome in oropharyngeal squamous cell carcinoma. J Nucl Med. 2012;53:1506–13.

    CAS  PubMed  Google Scholar 

  88. Kitajima K, Miyoshi Y, Sekine T, Takei H, Ito K, Suto A, et al. Harmonized pretreatment quantitative volume-based FDG-PET/CT parameters for prognosis of stage I-III breast cancer: Multicenter study. Oncotarget. 2021;12:95–105.

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the following colleagues for their kind support regarding the phantom experiments: Masafumi Ban, Ryuji Ikeda, Yuji Kojima, Akihito Kuroki, Takamasa Maeda, Yukito Maeda, Hiroyuki Nishida, Kazuki Nitta, Shinji Ochi, Hiroyoshi Okajima, Koji Osanai, Kazuhiro Otani, Shota Sakimoto, Minoru Sakurai, Takahiro Shiraishi, Yuji Tsutsui, Masaki Uno, Kei Wagatsuma, and Masanori Watanabe. The authors would like to thank Shohei Fukai, Noriaki Miyaji, Kazuki Motegi, and Takuro Umeda for visual assessment of phantom images. The authors appreciate the following two committees of the Japanese Society of Nuclear Medicine for their valuable support: the Expert Committee of Standardization of PET imaging (Members: Hiroshi Ito, Setsu Sakamoto, Tohru Shiga, Keiichi Matsumoto, and Hiroshi Watabe) and the PET Nuclear Medicine Committee (Members: Makoto Hosono, Masayuki Sasaki, Ukihide Tateishi, Kenji Ishii, Kengo Ito, Hiroshi Ito, Terue Okamura, Masami Kawamoto, Yuji Kuge, Ichiei Kuji, Michio Senda, Tadaki Nakahara, Yasuhiro Magata, Keiichi Matsumoto, Koji Murakami, Tsuyoshi Yoshida, and Atsuo Waki).

Funding

This study was supported in part by the National Cancer Center Research and Development Fund (2020-J-3), Foundation for Promotion of Cancer Research in Japan, the Japanese Society of Nuclear Medicine (JSNM) Working Group, and JSPS KAKENHI Grant Number JP20K08091.

Author information

Author notes

  1. Go Akamatsu and Naoki Shimada have contributed equally to this work.

Authors and Affiliations

  1. National Institutes for Quantum Science and Technology (QST), 4-9-1 Anagawa, Inage-ku, Chiba, 263-8555, Japan

    Go Akamatsu

  2. Cancer Institute Hospital, 3-8-31 Ariake, Koto, Tokyo, 135-8550, Japan

    Naoki Shimada & Takashi Terauchi

  3. Kyoto College of Medical Science, 1-3 Imakita, Oyamahigashi-cho, Sonobe-cho, Nantan, Kyoto, 622-0041, Japan

    Keiichi Matsumoto

  4. Gunma Prefectural College of Health Sciences, 323-1 Kamioki-machi, Maebashi, Gunma, 371-0052, Japan

    Hiromitsu Daisaki

  5. Dokkyo Medical University Hospital, 880 Kitakobayashi, Mibu, Shimotsugagun, Tochigi, 321-0293, Japan

    Kazufumi Suzuki

  6. Tohoku University, 6-3 Aoba, Aramaki, Aoba-ku, Sendai, Miyagi, 980-8578, Japan

    Hiroshi Watabe

  7. Hokkaido University of Science, 7-Jo 15-4-1 Maeda, Teine, Sapporo, Hokkaido, 006-8585, Japan

    Keiichi Oda

  8. Kobe City Medical Center General Hospital, 2-1-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan

    Michio Senda

  9. Tokyo Medical and Dental University School of Medicine, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan

    Ukihide Tateishi

Authors
  1. Go Akamatsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naoki Shimada
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keiichi Matsumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hiromitsu Daisaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kazufumi Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hiroshi Watabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Keiichi Oda
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michio Senda
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Takashi Terauchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ukihide Tateishi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Go Akamatsu or Naoki Shimada.

Ethics declarations

Conflict of interest

All authors declare that they have no conflicts of interest. All authors are members of the working group of the JSNM (no payment received).

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1660 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akamatsu, G., Shimada, N., Matsumoto, K. et al. New standards for phantom image quality and SUV harmonization range for multicenter oncology PET studies. Ann Nucl Med 36, 144–161 (2022). https://doi.org/10.1007/s12149-021-01709-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12149-021-01709-1

Keywords

Search

Navigation