Background-Based Delineation of Internal Tumor Volumes on Static Positron Emission Tomography in a Phantom Study

Document Type : Original Article


1 Department of Nuclear Medicine, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China

2 Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China

3 Guangdong Provincial Key Laboratory of Micro-nano Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, China

4 The PET-CT Center, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China


Objective(s): Considering the fact that the standardized uptake value (SUV) of a normal lung tissue is expressed as x±SD, x+3×SD could be considered as the threshold value to outline the internal tumor volume (ITV) of a lung neoplasm.
Methods: Three hollow models were filled with 55.0 kBq/mL fluorine18- fluorodeoxyglucose (18F-FDG) to represent tumors. The models were fixed to a barrel filled with 5.9 kBq/mL 18F-FDG to characterize normal lung tissues as a phantom. The PET/CT images of the phantom were acquired at rest. Then, the barrel was moved periodically to simulate breathing while acquiring PET/CT data. Volume recovery coefficient (VRC) was applied to evaluate the accuracy of ITVs. For statistical analysis, paired t-test and analysis of variance were applied.
Results: The VRCs ranged from 0.74 to 0.98 and significantly varied among gross tumor volumes for delineating ITV (P<0.01). In two-dimensional PET scans, the motion distance did not affect VRC (P>0.05), whereas VRC decreased with increasing distance in three-dimensional PET scans (P<0.05).
Conclusion: The threshold value (x+3×SD) had the potential to delineate the ITV of cancerous tissues, surrounded by lung tissues, particularly in two-dimensional PET images.    


Main Subjects


    1. Ettinger DS, Wood DE, Akerley W, Bazhenova LA, Borghaei H, Camidge DR, et al. Non-small cell lung cancer, version 1.2015. J Natl Compr Canc Netw. 2014;12(12):1738-61.
    2. Apostolova I, Wiemker R, Paulus T, Kabus S, Dreilich T, van den Hoff J, et al. Combined correction of recovery effect and motion blur for SUV quantification of solitary pulmonary nodules in FDG PET/CT. Eur Radiol. 2010;20(8):1868-77.
    3. Wang J, del Valle M, Goryawala M, Franquiz JM, McGoron AJ. Computer-assisted quantification of lung tumors in respiratory gated PET/CT images: phantom study. Med Biol Eng Comput. 2010;48(1):49-58.
    4. Bundschuh RA, Martí􀆴nez-Möller A, Essler M, Nekolla SG, Ziegler SI, Schwaiger M. Local motion correction for lung tumours in PET/CT--first results. Eur J Nucl Med Mol Imaging. 2008;35(11):1981-8.
    5. Schaefer A, Kremp S, Hellwig D, Rube 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;35(11):1989-99.
    6. Okubo M, Nishimura Y, Nakamatsu K, Okumura M, Shibata T, Kanamori S, et al. Static and moving phantom studies for radiation treatment planning in a positron emission tomography and computed tomography (PET/CT) system. Ann Nucl Med. 2008;22(7):579–86.
    7. Riegel AC, Bucci MK, Mawlawi OR, Johnson V, Ahmad M, Sun X, et al. Target definition of moving lung tumors in positron emission tomography: correlation of optimal activity concentration thresholds with object size, motion extent, and source-to-background ratio. Med Phys. 2010;37(4): 1742-52.
    8. Townsend DW. Dual-modality imaging: combining anatomy and function. J Nucl Med. 2008;49(6):938- 55.
    9. Chen Y, Chen X, Ji-An L, Li F. Estimation of internal tumor volume: a phantom study based on semiautomatics standardized uptake value of the background. Chinese J Med Imaging. 2015;23:91-5.
    10. Chen Y, Chen X, Li F, Ji-An L. Gross target volume delineation on PET images by a numerical approximation method–phantom studies. Nucl Electron Detect Technol. 2014;34:1463-8.
    11. Chen Y, Chen X, Li F, Ji-An L. Delineation gross tumor volume based on positron emission tomography images by a numerical approximation method. Ann Nucl Med. 2014;28(10):980-5.
    12. Meirelles GS, Kijewski P, Akhurst T. Correlation of PET/CT standardized uptake value measurements between dedicated workstations and a PACS-integrated workstation system. J Digit Imaging. 2007;20(3):307–13.
    13. Townsend DW. Dual-modality imaging: combining anatomy and function. J Nucl Med. 2008;49(6):938- 55.
    14. Chen Y, Zhang C, Xu H, Chen P, Fan M. Registered error between PET and CT images confirmed by a water model. Nucl Technique. 2012;35:619-23.
    15. Park SJ, Ionascu D, Killoran J, Mamede M, Gerbaudo VH, Chin L, et al. Evaluation of the combined effects of target size, respiratory motion and background activity on 3D and 4D PET/CT images. Phys Med Biol. 2008;53(13):3661-79.
    16. Fahey FH. Data acquisition in PET imaging. J Nucl Med Technol. 2002;30(2):39-49.