Dynamic 18F-FDG PET Images Simulation Using 4D-XCAT Phantom and Kinetic Modeling for Lesion Detectability Investigation and Scan Time Reduction Purpose

Document Type : Original Paper


1 Radiological and Atomic Physics Department, Physics Division, Nuclear Research Center of Algiers, 16000, Algiers, Algeria.

2 Atomic Energy Commission (COMENA), Algiers, Algeria.

3 Department of Physics, Faculty of Sciences, Ferhat Abbas-Setif1 University, Setif, ALGERIA


Introduction: Simulation in Positron Emission Tomography (PET) studies is considered as an effective approach to test new mathematical methods for image processing and lesion detection. It’s an alternative way to overcome the drawback of obtaining a sufficient set of clinical images with known truth about the presence or absence of lesions. This work aimed to simulate, in a new and fast way, realistic dynamic 18F-FDG PET images for lesion detectability investigation and scan time reduction.
Material and Methods: The 4D-XCAT phantom was utilized in this work. The three-compartment model was used to simulate the Time Activity Curves (TAC’s) of 18F-FDG. The arterial input function of 18F-FDG was modeled using a parametric function. The TAC’s of 11 tissues defined in the 4D-XCAT phantom were simulated. The activity values were calculated from the TAC’s considering a real 18F-FDG dynamic PET acquisition protocol. These activity values were assigned to each voxel of 4D-XCAT to produce 28 activity maps. The GE Discovery PET/CT 710 scanner, modeled in the STIR platform, was used to generate the sinograms. OSMAPOSL Algorithm was considered to reconstruct dynamic 18F-FDG PET images.
Results: Realistic dynamic 18F-FDG PET images were generated. The qualitative and quantitative comparison showed a good agreement between the 4D-XCAT phantom images before and after the reconstruction procedure. The computation time of the reconstruction procedure was 8.76 min/frame.
Conclusion: The present study was found to be a promising and realistic approach to dynamic PET dPET imaging optimization in terms of scanning time reduction and lesion detectability amelioration.


Main Subjects

  1. Besson FL, Fernandez B, Faure S, Mercier O, Seferian A, Mignard X, et al. 18 F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F] FDG PET-MRI data. EJNMMI research. 2020 Dec;10:1-3.
  2. Zhuang M, Karakatsanis NA, Dierckx RA, Zaidi H. Impact of tissue classification in MRI-guided attenuation correction on whole-body Patlak PET/MRI. Molecular Imaging and Biology. 2019 Dec;21:1147-56.
  3. Nozawa A, Rivandi AH, Kanematsu M, Hoshi H, Piccioni D, Kesari S, et al. Glucose-corrected standardized uptake value in the differentiation of high-grade glioma versus post-treatment changes. Nuclear medicine communications. 2015 Jun 1;36(6):573-81.
  4. Rusten E, Rødal J, Bruland ØS, Malinen E. Biologic targets identified from dynamic 18FDG-PET and implications for image-guided therapy. Acta Oncologica. 2013 Oct 1;52(7):1378-83.
  5. Dimitrakopoulou-Strauss A, Pan L, Sachpekidis C. Kinetic modeling and parametric imaging with dynamic PET for oncological applications: general considerations, current clinical applications, and future perspectives. European journal of nuclear medicine and molecular imaging. 2021 Jan;48:21-39.
  6. Al-Enezi MS, Bentourkia MH. Kinetic Modeling of Dynamic PET-¹⁸F-FDG Atherosclerosis Without Blood Sampling. IEEE Transactions on Radiation and Plasma Medical Sciences. 2020 Jun 30;4(6):729-34.
  7. Grkovski M, Schöder H, Lee NY, Carlin SD, Beattie BJ, Riaz N, et al. Multiparametric imaging of tumor hypoxia and perfusion with 18F-fluoromisonidazole dynamic PET in head and neck cancer. Journal of Nuclear Medicine. 2017 Jul 1;58(7):1072-80.
  8. Visser EP, Kienhorst LB, de Geus-Oei LF, Oyen WJ. Shortened dynamic FDG-PET protocol to determine the glucose metabolic rate in non-small cell lung carcinoma. In2008 IEEE Nuclear Science Symposium Conference Record. 2008:4455-8.
  9. Torizuka T, Nobezawa S, Momiki S, Kasamatsu N, Kanno T, Yoshikawa E, et al. Short dynamic FDG-PET imaging protocol for patients with lung cancer. European journal of nuclear medicine. 2000 Oct;27:1538-42.
  10. Fahrni G, Karakatsanis NA, Di Domenicantonio G, Garibotto V, Zaidi H. Does whole-body Patlak 18 F-FDG PET imaging improve lesion detectability in clinical oncology?. European radiology. 2019 Sep 1;29:4812-21.
  11. Lee C. Monte carlo calculations in nuclear medicine second edition: Applications in diagnostic imaging. 2014; 431-2
  12. Karakatsanis NA, Lodge MA, Tahari AK, Zhou Y, Wahl RL, Rahmim A. Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application: Physics in Medicine & Biology. 2013 Sep 30;58(20):7391.
  13. Häggström I, Beattie BJ, Schmidtlein CR. Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies. Medical Physics. 2016 Jun;43(6Part1):3104-16.
  14. Karakatsanis NA, Zhou Y, Lodge MA, Casey ME, Wahl RL, Zaidi H, et al. Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET. Physics in Medicine & Biology. 2015 Oct 28;60(22):8643.
  15. Segars WP, Sturgeon G, Mendonca S, Grimes J, Tsui BM. 4D XCAT phantom for multimodality imaging research. Medical physics. 2010 Sep;37(9):4902-15.
  16. Segars WP, Tsui BM. MCAT to XCAT: The evolution of 4-D computerized phantoms for imaging research. Proceedings of the IEEE. 2009 Nov 17;97(12):1954-68.
  17. Segars WP, Tsui BM, Cai J, Yin FF, Fung GS, Samei E. Application of the 4-D XCAT phantoms in biomedical imaging and beyond. IEEE transactions on medical imaging. 2017 Aug 10;37(3):680-92.
  18. Nankali S, Torshabi AE, Miandoab PS, Baghizadeh A. Optimum location of external markers using feature selection algorithms for real‐time tumor tracking in external‐beam radiotherapy: a virtual phantom study. Journal of applied clinical medical physics. 2016 Jan;17(1):221-33.
  19. Miandoab PS, Torshabi AE, Nankali S. Investigation of the optimum location of external markers for patient setup accuracy enhancement at external beam radiotherapy. Journal of applied clinical medical physics. 2016 Nov;17(6):32-43.
  20. Nankali S, Torshabi AE, Miandoab PS. A feasibility study on ribs as anatomical landmarks for motion tracking of lung and liver tumors at external beam radiotherapy. Technology in cancer research & treatment. 2017 Feb;16(1):99-111.
  21. Qiao H, Bai J. Dynamic simulation of FDG-PET image based on VHP datasets. InThe 2011 IEEE/ICME International Conference on Complex Medical Engineering. 2011 May 22 (pp. 154-158). IEEE.
  22. Dimitrakopoulou-Strauss A, Georgoulias V, Eisenhut M, Herth F, Koukouraki S, Mäcke HR, Haberkorn U, et al. Quantitative assessment of SSTR2 expression in patients with non-small cell lung cancer using 68 Ga-DOTATOC PET and comparison with 18 F-FDG PET. European journal of nuclear medicine and molecular imaging. 2006 Jul;33:823-30.
  23. Wienhard K. Measurement of glucose consumption using [18F] fluorodeoxyglucose. Methods. 2002 Jul 1;27(3):218-25.
  24. Sokoloff L, Reivich M, Kennedy C, Rosiers MD, Patlak CS, Pettigrew KE, et al. The [14C] deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and anesthetized albino rat 1. Journal of neurochemistry. 1977 May;28(5):897-916.
  25. Feng D, Huang SC, Wang X. Models for computer simulation studies of input functions for tracer kinetic modeling with positron emission tomography. International journal of bio-medical computing. 1993 Mar 1;32(2):95-110.
  26. Feng D, Wang X. A computer simulation study on the effects of input function measurement noise in tracer kinetic modeling with positron emission tomography (PET). Computers in biology and medicine. 1993 Jan 1;23(1):57-68.
  27. Feng D, Li X, Huang SC. A new double modeling approach for dynamic cardiac PET studies using noise and spillover contaminated LV measurements. IEEE transactions on biomedical engineering. 1996 Mar;43(3):319-27.
  28. Feng D, Wang X. A method for biomedical system modelling and physiological parameter estimation using indirectly measured input functions. International journal of systems science. 1995 Apr 1;26(4):723-39.
  29. Thielemans K, Tsoumpas C, Mustafovic S, Beisel T, Aguiar P, Dikaios N, Jacobson MW. STIR: software for tomographic image reconstruction release 2. Physics in Medicine & Biology. 2012 Jan 31;57(4):867.
  30. Loening AM, Gambhir SS. AMIDE: a free software tool for multimodality medical image analysis. Molecular imaging. 2003 Jul 1;2(3):15353500200303133.