Journal of Nuclear Science, Engineering and Technology (JONSAT)
In cooperation with the Iranian Nuclear Society

Production of parametric Ki images by dual time point (two 3 min clinical routine static scans)

Document Type : Research Paper

Authors

N. Reshtebar 1
S,A. Hosseini 1
P. Sheikhzadeh 2

1 Faculty of Energy Engineering, Sharif University of Technology, P.O.Box: 1114-14565, Tehran - Iran

2 Department of Nuclear Medicine, Imam Khomeini Hospital, Tehran University of Medical Sciences, P.O.Box: 1419733141, Tehran - Iran

https://doi.org/10.24200/nst.2023.1584
Abstract
Dynamic Positron Emission Tomography (PET) imaging has significant potential for extracting kinetic parameters of tracers, particularly the Ki parameter. This study evaluates the use of the Dual Time Point (DTP) technique to generate parametric Ki images from two 3-minute static PET scans. A simulation study was conducted using the XCAT phantom, generating six realistic heterogeneous tumors embedded in lung and liver tissues with various levels of [18F] FDG uptake. Parametric Ki images were generated and evaluated using Patlak analysis and a population-based input function (PBIF). Additionally, TBR and CNR parameters in SUV images and parametric images produced by DTP and full dynamic methods were compared and analyzed. The results showed a significant correlation (> 0.9) between the Ki parameter derived from DTP and full dynamic imaging methods. Moreover, the high TBR parameter in DTP images compared to SUV images (70% for lung tumors, 35% for liver tumors) indicates improved contrast and image quality. Consequently, DTP images can be a suitable alternative to complete dynamic PET and SUV images in clinical settings.

Highlights

  1. Rahmim A, Lodge M.A, Karakatsanis N.A, Panin V.Y, Zhou Y, McMillan A, Cho S, Zaidi H, Casey M.E, Wahl R.L. Dynamic whole-body PET imaging: principles, potentials and applications. Eur J Nucl Med Mol Imaging. 2019;46:501–18. doi:10.1007/s00259-018-4153-6.

 

  1. Viswanath V, Chitalia R, Pantel A.R, Karp J.S, Mankoff D.A. Analysis of Four-Dimensional Data for Total Body PET Imaging. PET Clin. 2021;16:55–64. doi:10.1016/j.cpet.2020.09.009.

 

  1. Freedman N.M.T, Sundaram S.K, Kurdziel K, Carrasquillo J.A, Whatley M, Carson J.M, Sellers D, Libutti S.K, Yang J.C, Bacharach S.L. Comparison of SUV and Patlak slope for monitoring of cancer therapy using serial PET scans. Eur J Nucl Med Mol Imaging. 2003;30:46–53.doi:10.1007/s00259-002-0981-4.

 

  1. Shreve P.D, Anzai Y, Wahl R.L. Pitfalls in oncologic diagnosis with FDG PET imaging: Physiologic and benign variants. Radiographics. 1999;19:61–77. doi:10.1148/radiographics.19.1.g99ja0761.

 

  1. Zaidi H, Karakatsanis N. Towards enhanced PET quantification in clinical oncology. Br J Radiol. 2018;91:20170508. doi:10.1259/bjr.20170508.

 

  1. Zhuang M, Karakatsanis N.A, Dierckx R.A.J.O, Zaidi H. Quantitative Analysis of Heterogeneous [18F]FDG Static (SUV) vs. Patlak (Ki) Whole-body PET Imaging Using Different Segmentation Methods: a Simulation Study. Mol Imaging Biol. 2019;21:317–27.

 

  1. Dimitrakopoulou-Strauss A, Pan L, Strauss L.G. Quantitative approaches of dynamic FDG-PET and PET/CT studies (dPET/CT) for the evaluation of oncological patients. Cancer Imaging. 2012;12:283–9. doi:10.1102/1470-7330.2012.0033.

 

  1. Strauss L.G, Dimitrakopoulou-Strauss A, Haberkorn U. Shortened PET data acquisition protocol for the quantification of 18F-FDG kinetics. J Nucl Med. 2003;44:1933–9.

 

  1. Visser E.P, Kienhorst L.B.E, De Geus-Oei L.F, Oyen W.J.G. Shortened dynamic FDG-PET protocol to determine the glucose metabolic rate in non-small cell lung carcinoma. IEEE Nucl Sci Symp Conf Rec. 2008:4455–8. doi:10.1109/NSSMIC.2008.4774271.

 

  1. Strauss L.G, Pan L, Cheng C, Haberkorn U, Dimitrakopoulou-Strauss A. Shortened acquisition protocols for the quantitative assessment of the 2-tissue-compartment model using dynamic PET/CT18F-FDG studies. J Nucl Med. 2011;52:379-385. doi:10.2967/jnumed.110.079798.

 

  1. Samimi R, Kamali-Asl A, Geramifar P, Van Den Hoff J, Rahmim A. Short-duration dynamic FDG PET imaging: Optimization and clinical application. Phys Medica. 2020;80:193–200. https://doi.org/10.1016/j.ejmp.2020.11.004.

 

  1. Segars W.P, Sturgeon G, Mendonca S, Grimes J, Tsui B.M.W. 4D XCAT phantom for multimodality imaging research. Med Phys. 2010;37:4902–15. doi:10.1118/1.3480985.

 

  1. Karakatsanis N.A, Lodge M.A, Tahari A.K, Zhou Y, Wahl R.L, Rahmim A. Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application. Phys Med Biol. 2013;58:7391–418. doi:10.1088/0031-9155/58/20/7391.

 

  1. Tonietto M, Zanderigo F, Bertoldo A, Devanand D.P, Mann J.J, Bodini B, Stankoff B. Multicenter validation of population-based input function with non-linear mixed effect modeling for voxel-wise quantification of [18F]Fdg metabolic rate. Proc - Int Symp Biomed Imaging. 2019;2019–April:376–9. doi:10.1109/ISBI.2019.8759190.

 

  1. Feng D, Huang S.C, Wang X. Models for computer simulation studies of input functions for tracer kinetic modeling with positron emission tomography. Int J Biomed Comput. 1993;32:95–110. doi:10.1016/0020-7101(93)90049-C.

 

  1. Le Maitre A, Segars W.P, Marache S, Reilhac A, Hatt M, Tomei S, Lartizien C, Visvikis D. Incorporating patient-specific variability in the simulation of realistic whole-body 18F-FDG distributions for oncology applications. Proc IEEE. 2009;97:2026–38. doi:10.1109/JPROC.2009.2027925.

 

  1. Wanet M, Lee J.A, Weynand B, De Bast M, Poncelet A, Lacroix V, Coche E, Grégoire V, Geets X. Gradient-based delineation of the primary GTV on FDG-PET in non-small cell lung cancer: A comparison with threshold-based approaches, CT and surgical specimens. Radiother Oncol. 2011;98:117–25. doi:10.1016/j.radonc.2010.10.006.

 

  1. Ashrafinia S, Mohy-Ud-Din H, Karakatsanis N.A, Jha A.K, Casey M.E, Kadrmas D.J, Rahmim A. Generalized PSF modeling for optimized quantitation in PET imaging. Phys Med Biol. 2017;62:5149–79. https://doi.org/10.1088/1361-6560/aa6911.

 

  1. Cai W, Feng D, Fulton R, Siu W.C. Generalized linear least squares algorithms for modeling glucose metabolism in the human brain with corrections for vascular effects. Comput Methods Programs Biomed. 2002;68:1–14. doi:10.1016/S0169-2607(01)00160-2.

 

  1. Im H.J, Bradshaw T, Solaiyappan M, Cho S.Y. Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better? Nucl Med Mol Imaging (2010). 2018;52:5–15. doi:10.1007/s13139-017-0493-6.

 

  1. Zaker N, Kotasidis F, Garibotto V, Zaidi H. Assessment of Lesion Detectability in Dynamic Whole-Body PET Imaging Using Compartmental and Patlak Parametric Mapping. Clin Nucl Med. 2020;45:E221–31. https://doi.org/10.1097/RLU.0000000000002954.

 

  1. Ilan E, Sandström M, Velikyan I, Sundin A, Eriksson B, Lubberink M. Parametric net influx rate images of 68Ga-DOTATOC and 68Ga-DOTATATE: Quantitative accuracy and improved image contrast. J Nucl Med. 2017;58:744–9. https://doi.org/10.2967/jnumed.116.180380.

Keywords

Compartmental modeling
Positron emission tomography
Dynamic imaging
XCAT phantom
Dual time point technique
  1. Rahmim A, Lodge M.A, Karakatsanis N.A, Panin V.Y, Zhou Y, McMillan A, Cho S, Zaidi H, Casey M.E, Wahl R.L. Dynamic whole-body PET imaging: principles, potentials and applications. Eur J Nucl Med Mol Imaging. 2019;46:501–18. doi:10.1007/s00259-018-4153-6.

 

  1. Viswanath V, Chitalia R, Pantel A.R, Karp J.S, Mankoff D.A. Analysis of Four-Dimensional Data for Total Body PET Imaging. PET Clin. 2021;16:55–64. doi:10.1016/j.cpet.2020.09.009.

 

  1. Freedman N.M.T, Sundaram S.K, Kurdziel K, Carrasquillo J.A, Whatley M, Carson J.M, Sellers D, Libutti S.K, Yang J.C, Bacharach S.L. Comparison of SUV and Patlak slope for monitoring of cancer therapy using serial PET scans. Eur J Nucl Med Mol Imaging. 2003;30:46–53.doi:10.1007/s00259-002-0981-4.

 

  1. Shreve P.D, Anzai Y, Wahl R.L. Pitfalls in oncologic diagnosis with FDG PET imaging: Physiologic and benign variants. Radiographics. 1999;19:61–77. doi:10.1148/radiographics.19.1.g99ja0761.

 

  1. Zaidi H, Karakatsanis N. Towards enhanced PET quantification in clinical oncology. Br J Radiol. 2018;91:20170508. doi:10.1259/bjr.20170508.

 

  1. Zhuang M, Karakatsanis N.A, Dierckx R.A.J.O, Zaidi H. Quantitative Analysis of Heterogeneous [18F]FDG Static (SUV) vs. Patlak (Ki) Whole-body PET Imaging Using Different Segmentation Methods: a Simulation Study. Mol Imaging Biol. 2019;21:317–27.

 

  1. Dimitrakopoulou-Strauss A, Pan L, Strauss L.G. Quantitative approaches of dynamic FDG-PET and PET/CT studies (dPET/CT) for the evaluation of oncological patients. Cancer Imaging. 2012;12:283–9. doi:10.1102/1470-7330.2012.0033.

 

  1. Strauss L.G, Dimitrakopoulou-Strauss A, Haberkorn U. Shortened PET data acquisition protocol for the quantification of 18F-FDG kinetics. J Nucl Med. 2003;44:1933–9.

 

  1. Visser E.P, Kienhorst L.B.E, De Geus-Oei L.F, Oyen W.J.G. Shortened dynamic FDG-PET protocol to determine the glucose metabolic rate in non-small cell lung carcinoma. IEEE Nucl Sci Symp Conf Rec. 2008:4455–8. doi:10.1109/NSSMIC.2008.4774271.

 

  1. Strauss L.G, Pan L, Cheng C, Haberkorn U, Dimitrakopoulou-Strauss A. Shortened acquisition protocols for the quantitative assessment of the 2-tissue-compartment model using dynamic PET/CT18F-FDG studies. J Nucl Med. 2011;52:379-385. doi:10.2967/jnumed.110.079798.

 

  1. Samimi R, Kamali-Asl A, Geramifar P, Van Den Hoff J, Rahmim A. Short-duration dynamic FDG PET imaging: Optimization and clinical application. Phys Medica. 2020;80:193–200. https://doi.org/10.1016/j.ejmp.2020.11.004.

 

  1. Segars W.P, Sturgeon G, Mendonca S, Grimes J, Tsui B.M.W. 4D XCAT phantom for multimodality imaging research. Med Phys. 2010;37:4902–15. doi:10.1118/1.3480985.

 

  1. Karakatsanis N.A, Lodge M.A, Tahari A.K, Zhou Y, Wahl R.L, Rahmim A. Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application. Phys Med Biol. 2013;58:7391–418. doi:10.1088/0031-9155/58/20/7391.

 

  1. Tonietto M, Zanderigo F, Bertoldo A, Devanand D.P, Mann J.J, Bodini B, Stankoff B. Multicenter validation of population-based input function with non-linear mixed effect modeling for voxel-wise quantification of [18F]Fdg metabolic rate. Proc - Int Symp Biomed Imaging. 2019;2019–April:376–9. doi:10.1109/ISBI.2019.8759190.

 

  1. Feng D, Huang S.C, Wang X. Models for computer simulation studies of input functions for tracer kinetic modeling with positron emission tomography. Int J Biomed Comput. 1993;32:95–110. doi:10.1016/0020-7101(93)90049-C.

 

  1. Le Maitre A, Segars W.P, Marache S, Reilhac A, Hatt M, Tomei S, Lartizien C, Visvikis D. Incorporating patient-specific variability in the simulation of realistic whole-body 18F-FDG distributions for oncology applications. Proc IEEE. 2009;97:2026–38. doi:10.1109/JPROC.2009.2027925.

 

  1. Wanet M, Lee J.A, Weynand B, De Bast M, Poncelet A, Lacroix V, Coche E, Grégoire V, Geets X. Gradient-based delineation of the primary GTV on FDG-PET in non-small cell lung cancer: A comparison with threshold-based approaches, CT and surgical specimens. Radiother Oncol. 2011;98:117–25. doi:10.1016/j.radonc.2010.10.006.

 

  1. Ashrafinia S, Mohy-Ud-Din H, Karakatsanis N.A, Jha A.K, Casey M.E, Kadrmas D.J, Rahmim A. Generalized PSF modeling for optimized quantitation in PET imaging. Phys Med Biol. 2017;62:5149–79. https://doi.org/10.1088/1361-6560/aa6911.

 

  1. Cai W, Feng D, Fulton R, Siu W.C. Generalized linear least squares algorithms for modeling glucose metabolism in the human brain with corrections for vascular effects. Comput Methods Programs Biomed. 2002;68:1–14. doi:10.1016/S0169-2607(01)00160-2.

 

  1. Im H.J, Bradshaw T, Solaiyappan M, Cho S.Y. Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better? Nucl Med Mol Imaging (2010). 2018;52:5–15. doi:10.1007/s13139-017-0493-6.

 

  1. Zaker N, Kotasidis F, Garibotto V, Zaidi H. Assessment of Lesion Detectability in Dynamic Whole-Body PET Imaging Using Compartmental and Patlak Parametric Mapping. Clin Nucl Med. 2020;45:E221–31. https://doi.org/10.1097/RLU.0000000000002954.

 

  1. Ilan E, Sandström M, Velikyan I, Sundin A, Eriksson B, Lubberink M. Parametric net influx rate images of 68Ga-DOTATOC and 68Ga-DOTATATE: Quantitative accuracy and improved image contrast. J Nucl Med. 2017;58:744–9. https://doi.org/10.2967/jnumed.116.180380.

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