The influence of using different reconstruction algorithms on sensitivity of quantitative 18F-FDG-PET volumetric measures to background activity variation

Document Type : Original Article


1 Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran

2 Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran

3 Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran

4 PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

5 Medical Imaging Research Center and Physics Unit, Department of Radiotherapy and Oncology, Namazi Hospital, Shiraz University of Medical Sciences, Shiraz, Iran

6 Department of Medical Physics, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran


Introduction: This study aims to investigate the influence of background activity variation on image quantification in differently reconstructed PET/CT images.
Methods: Measurements were performed on a Discovery-690 PET/CT scanner using a custom-built NEMA-like phantom. A background activity level of 5.3 and 2.6 kBq/ml 18F-FDG were applied. Images were reconstructed employing four different reconstruction algorithms: HD (OSEM with no PSF or TOF), PSF only, TOF only, and TOFPSF, with Gaussian filters of 3 and 6.4 mm in FWHM. SUVmax and SUVpeak were obtained and used as cut-off thresholding; Metabolic Tumor Volume (MTV) and Total Lesion Glycolysis (TLG) were measured. The volume recovery coefficients (VRCs), the relative percent error (ΔMTV), and Dice similarity coefficient were assessed with respect to true values.
Results:SUVmax and SUVpeak decreased and MTV increased as function of increasing the background dose. The most differences occur in smaller volumes with 3-mm filter; Non-TOF and Non-PSF reconstruction methods were more sensitive to increasing the background activity in the smaller and larger volumes, respectively. The TLG values were affected in the small lesions (decrease up to 12%). In a range of target volumes, differences between the mean ΔMTV in the high and low background dose varied from -11.8% to 7.2% using SUVmax and from 2.1% to 7.6% using SUVpeak inter reconstruction methods.
Conclusion: The effect of the background activity variation on SUV-based quantification in small lesion was more noticeable than large lesion. The HD and TOFPSF algorithms had the lowest and the highest sensitivity to background activity, respectively.


Main Subjects

  1. 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 May;50 Suppl 1:122S-50S.
  2. Thorwarth D, Geets X, Paiusco M. Physical radiotherapy treatment planning based on functional PET/CT data. Radiother Oncol. 2010 Sep;96(3):317-24.
  3. Tofilon PJ, Camphausen K. Increasing the therapeutic ratio of radiotherapy: Springer; 2017.
  4. Lucignani G. SUV and segmentation: pressing challenges in tumour assessment and treatment. Eur J Nucl Med Mol Imaging. 2009 Apr;36(4):715-20.
  5. Oh JR, Seo JH, Chong A, Min JJ, Song HC, Kim YC, Bom HS. Whole-body metabolic tumour volume of 18F-FDG PET/CT improves the prediction of prognosis in small cell lung cancer. Eur J Nucl Med Mol Imaging. 2012 Jun;39(6):925-35.
  6. Lee JW, Kang CM, Choi HJ, Lee WJ, Song SY, Lee JH, Lee JD. Prognostic Value of Metabolic Tumor Volume and Total Lesion Glycolysis on Preoperative ¹⁸F-FDG PET/CT in Patients with Pancreatic Cancer. J Nucl Med. 2014 Jun;55(6):898-904.
  7. Sheikhbahaei S, Marcus C, Subramaniam RM. 18F FDG PET/CT and head and neck cancer: Patient management and outcomes. PET Clin. 2015 Apr;10(2):125-45.
  8. Manca G, Vanzi E, Rubello D, Giammarile F, Grassetto G, Wong KK, Perkins AC, Colletti PM, Volterrani D. (18)F-FDG PET/CT quantification in head and neck squamous cell cancer: principles, technical issues and clinical applications. Eur J Nucl Med Mol Imaging. 2016 Jul;43(7):1360-75.
  9. Larson SM, Erdi Y, Akhurst T, Mazumdar M, Macapinlac HA, Finn RD, Casilla C, Fazzari M, Srivastava N, Yeung HW, Humm JL, Guillem J, Downey R, Karpeh M, Cohen AE, Ginsberg R. Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging. The Visual Response Score and the Change in Total Lesion Glycolysis. Clin Positron Imaging. 1999 May;2(3):159-171.
  10. d'Amico A. Review of clinical practice utility of positron emission tomography with 18F-fluorodeoxyglucose in assessing tumour response to therapy. Radiol Med. 2015 Apr;120(4):345-51.
  11. Mac Manus MP, Everitt S, Bayne M, Ball D, Plumridge N, Binns D, Herschtal A, Cruickshank D, Bressel M, Hicks RJ. The use of fused PET/CT images for patient selection and radical radiotherapy target volume definition in patients with non-small cell lung cancer: results of a prospective study with mature survival data. Radiother Oncol. 2013 Mar;106(3):292-8.
  12. Pan T, Mawlawi O. PET/CT in radiation oncology. Med Phys. 2008 Nov;35(11):4955-66.
  13. Shi X, Meng X, Sun X, Xing L, Yu J. PET/CT imaging-guided dose painting in radiation therapy. Cancer Lett. 2014 Dec 28;355(2):169-75.
  14. Grégoire V, Langendijk JA, Nuyts S. Advances in Radiotherapy for Head and Neck Cancer. J Clin Oncol. 2015 Oct 10;33(29):3277-84.
  15. Ling CC, Humm J, Larson S, Amols H, Fuks Z, Leibel S, Koutcher JA. Towards multidimensional radiotherapy (MD-CRT): biological imaging and biological conformality. Int J Radiat Oncol Biol Phys. 2000 Jun 1;47(3):551-60.
  16. Bentzen SM. Theragnostic imaging for radiation oncology: dose-painting by numbers. Lancet Oncol. 2005 Feb;6(2):112-7.
  17. Lu W, Wang J, Zhang HH. Computerized PET/CT image analysis in the evaluation of tumour response to therapy. Br J Radiol. 2015 Apr;88(1048):20140625.
  18. Peeken JC, Nüsslin F, Combs SE. "Radio-oncomics" : The potential of radiomics in radiation oncology. Strahlenther Onkol. 2017 Oct;193(10):767-779.
  19. Zhang H, Tan S, Chen W, Kligerman S, Kim G, D'Souza WD, Suntharalingam M, Lu W. Modeling pathologic response of esophageal cancer to chemoradiation therapy using spatial-temporal 18F-FDG PET features, clinical parameters, and demographics. Int J Radiat Oncol Biol Phys. 2014 Jan 1;88(1):195-203.
  20. Doot RK, McDonald ES, Mankoff DA. Role of PET quantitation in the monitoring of cancer response to treatment: Review of approaches and human clinical trials. Clin Transl Imaging. 2014 Aug 1;2(4):295-303.
  21. Xu W, Yu S, Ma Y, Liu C, Xin J. Effect of different segmentation algorithms on metabolic tumor volume measured on 18F-FDG PET/CT of cervical primary squamous cell carcinoma. Nucl Med Commun. 2017 Mar;38(3):259-265.
  22. Berthon B, Evans M, Marshall C, Palaniappan N, Cole N, Jayaprakasam V, Rackley T, Spezi E. Head and neck target delineation using a novel PET automatic segmentation algorithm. Radiother Oncol. 2017 Feb;122(2):242-247.
  23. Sridhar P, Mercier G, Tan J, Truong MT, Daly B, Subramaniam RM. FDG PET metabolic tumor volume segmentation and pathologic volume of primary human solid tumors. AJR Am J Roentgenol. 2014 May;202(5):1114-9.
  24. Zaidi H, El Naqa I. PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques. Eur J Nucl Med Mol Imaging. 2010 Nov;37(11):2165-87.
  25. Rogasch JM, Hofheinz F, Lougovski A, Furth C, Ruf J, Großer OS, Mohnike K, Hass P, Walke M, Amthauer H, Steffen IG. 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 Dec;1(1):12.
  26. Brendle C, Kupferschläger J, Nikolaou K, la Fougère C, Gatidis S, Pfannenberg C. Is the standard uptake value (SUV) appropriate for quantification in clinical PET imaging? - Variability induced by different SUV measurements and varying reconstruction methods. Eur J Radiol. 2015 Jan;84(1):158-162.
  27. Knudtsen IS, van Elmpt W, Ollers M, Malinen E. Impact of PET reconstruction algorithm and threshold on dose painting of non-small cell lung cancer. Radiother Oncol. 2014 Nov;113(2):210-4.
  28. Soret M, Bacharach SL, Buvat I. Partial-volume effect in PET tumor imaging. J Nucl Med. 2007 Jun;48(6):932-45.
  29. Chen GH, Yao ZF, Fan XW, Zhang YJ, Gao HQ, Qian W, Wu KL, Jiang GL. Variation in background intensity affects PET-based gross tumor volume delineation in non-small-cell lung cancer: the need for individualized information. Radiother Oncol. 2013 Oct;109(1):71-6.
  30. Foster B, Bagci U, Mansoor A, Xu Z, Mollura DJ1. A review on segmentation of positron emission tomography images. Comput Biol Med. 2014 Jul;50:76-96.
  31. Andersen FL, Klausen TL, Loft A, Beyer T, Holm S. Clinical evaluation of PET image reconstruction using a spatial resolution model. Eur J Radiol. 2013 May;82(5):862-9.
  32. Prieto E, Domínguez-Prado I, García-Velloso MJ, Peñuelas I, Richter JÁ, Martí-Climent JM. Impact of time-of-flight and point-spread-function in SUV quantification for oncological PET. Clin Nucl Med. 2013 Feb;38(2):103-9.
  33. Sheikhbahaei S, Marcus C, Wray R, Rahmim A, Lodge MA, Subramaniam RM. Impact of point spread function reconstruction on quantitative 18F-FDG-PET/CT imaging parameters and inter-reader reproducibility in solid tumors. Nucl Med Commun. 2016 Mar;37(3):288-96.
  34. Lee NY, Riaz N, Lu JJ. Target volume delineation for conformal and intensity-modulated radiation therapy: Springer; 2014.
  35. Hanna GG, Hounsell AR, O'Sullivan JM. Geometrical analysis of radiotherapy target volume delineation: a systematic review of reported comparison methods. Clin Oncol (R Coll Radiol). 2010 Sep;22(7):515-25.
  36. Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, Verzijlbergen FJ, Barrington SF, Pike LC, Weber WA, Stroobants S, Delbeke D, Donohoe KJ, Holbrook S, Graham MM, Testanera G, Hoekstra OS, Zijlstra J, Visser E, Hoekstra CJ, Pruim J, Willemsen A, Arends B, Kotzerke J, Bockisch A, Beyer T, Chiti A, Krause BJ; European Association of Nuclear Medicine (EANM). FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015 Feb;42(2):328-54.
  37. Zou KH, Warfield SK, Bharatha A, Tempany CMC, Kaus MR, Haker SJ, Wells WM, Jolesz FA, Kikinis R. Statistical validation of image segmentation quality based on a spatial overlap index 1: Scientific reports. Acad Radiol. 2004;11(2):178-89.
  38. Sadick M, Molina F, Frey S, Piniol R, Sadick H, Brade J, Fink C, Schoenberg SO, He Y. Effect of reconstruction parameters in high-definition PET/CT on assessment of lymph node metastases in head and neck squamous cell carcinoma. J Nucl Med Technol. 2013 Mar;41(1):19-25.
  39. Schaefer A, Kremp S, Hellwig D, Rübe C, Kirsch CM, Nestle U. A contrast-oriented algorithm for FDG-PET-based delineation of tumour volumes for the radiotherapy of lung cancer: derivation from phantom measurements and validation in patient data. Eur J Nucl Med Mol Imaging. 2008 Nov;35(11):1989-99.
  40. Burger IA, Vargas HA, Beattie BJ, Goldman DA, Zheng J, Larson SM, Humm JL, Schmidtlein CR. How to assess background activity: introducing a histogram-based analysis as a first step for accurate one-step PET quantification. Nucl Med Commun. 2014 Mar;35(3):316-24.
  41. Burger IA, Casanova R, Steiger S, Husmann L, Stolzmann P, Huellner MW, Curioni A, Hillinger S, Schmidtlein CR, Soltermann A. 18F-FDG PET/CT of Non-Small Cell Lung Carcinoma Under Neoadjuvant Chemotherapy: Background-Based Adaptive-Volume Metrics Outperform TLG and MTV in Predicting Histopathologic Response. J Nucl Med. 2016 Jun;57(6):849-54.