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First results on kinetic modelling and parametric imaging of dynamic 18F-FDG datasets from a long axial FOV PET scanner in oncological patients

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Abstract

Purpose

To investigate the kinetics of 18F-fluorodeoxyglucose (18F-FDG) by positron emission tomography (PET) in multiple organs and test the feasibility of total-body parametric imaging using an image-derived input function (IDIF).

Methods

Twenty-four oncological patients underwent dynamic 18F-FDG scans lasting 65 min using a long  axial FOV (LAFOV) PET/CT system. Time activity curves (TAC) were extracted from semi-automated segmentations of multiple organs, cerebral grey and white matter, and from vascular structures. The tissue and tumor lesion TACs were fitted using an irreversible two-tissue compartment (2TC) and a Patlak model. Parametric images were also generated using direct and indirect Patlak methods and their performances were evaluated.

Results

We report estimates of kinetic parameters and metabolic rate of glucose consumption (MRFDG) for different organs and tumor lesions. In some organs, there were significant differences between MRFDG values estimated using 2TC and Patlak models. No statistically significant difference was seen between MRFDG values estimated using 2TC and Patlak methods in tumor lesions (paired t-test, P = 0.65). Parametric imaging showed that net influx (Ki) images generated using direct and indirect Patlak methods had superior tumor-to-background ratio (TBR) to standard uptake value (SUV) images (3.1- and 3.0-fold mean increases in TBRmean, respectively). Influx images generated using the direct Patlak method had twofold higher contrast-to-noise ratio in tumor lesions compared to images generated using the indirect Patlak method.

Conclusion

We performed pharmacokinetic modelling of multiple organs using linear and non-linear models using dynamic total-body 18F-FDG images. Although parametric images did not reveal more tumors than SUV images, the results confirmed that parametric imaging furnishes improved tumor contrast. We thus demonstrate the feasibility of total-body kinetic modelling and parametric imaging in basic research and oncological studies. LAFOV PET can enhance dynamic imaging capabilities by providing high sensitivity parametric images and allowing total-body pharmacokinetic analysis.

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References

  1. Phelps ME, Huang SC, Hoffman EJ, Selin C, Sokoloff L, Kuhl DE. Tomographic measurement of local cerebral glucose metabolic rate in humans with (F-18)2-fluoro-2-deoxy-D-glucose: Validation of method. Ann Neurol. 1979;6:371–88.

    Article  CAS  PubMed  Google Scholar 

  2. Reivich M, Kuhl D, Wolf A. The [18F]fluorodeoxyglucose method for the measurement of local cerebral glucose utilization in man. Circ Res. 1979;44:127–37.

    Article  CAS  PubMed  Google Scholar 

  3. Karakatsanis NA, Casey ME, Lodge MA, Rahmim A, Zaidi H. Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation-maximization reconstruction. Phys Med Biol. 2016;61:5456–85.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Tsoumpas C, Turkheimer FE, Thielemans K. Study of direct and indirect parametric estimation methods of linear models in dynamic positron emission tomography. Med Phys. 2008;35:1299–309.

    Article  PubMed  Google Scholar 

  5. Wang G, Fu L, Qi J. Maximum a posteriori reconstruction of the Patlak parametric image from sinograms in dynamic PET. Phys Med Biol. 2008;53:593–604.

    Article  PubMed  Google Scholar 

  6. Dias AH, Pedersen MF, Danielsen H, Munk OL, Gormsen LC. Clinical feasibility and impact of fully automated multiparametric PET imaging using direct Patlak reconstruction: evaluation of 103 dynamic whole-body 18F-FDG PET/CT scans. Eur J Nucl Med Mol Imaging. 2021;48:837–50.

    Article  PubMed  Google Scholar 

  7. Gunn RN, Gunn SR, Cunningham VJ. Positron emission tomography compartmental models. J Cereb Blood Flow Metab. 2001;21:635–52.

    Article  CAS  PubMed  Google Scholar 

  8. Schmidt K, Lucignani G, Moresco RM, Rizzo G, Gilardi MC, Messa C, et al. Errors introduced by tissue heterogeneity in estimation of local cerebral glucose utilization with current kinetic models of the [18F]fluorodeoxyglucose method. J Cereb Blood Flow Metab. 1992;12:823–34.

    Article  CAS  PubMed  Google Scholar 

  9. Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab. 1983;3:1–7.

    Article  CAS  PubMed  Google Scholar 

  10. Patlak CS, Blasberg RG. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data, Generalizations. J Cereb Blood Flow Metab. 1985;5:584–90.

    Article  CAS  PubMed  Google Scholar 

  11. Huang SC, Phelps ME, Hoffman EJ, Sideris K, Selin CJ, Kuhl DE. Noninvasive determination of local cerebral metabolic rate of glucose in man. Am J Physiol. 1980;238;E69-E82.

  12. Zanotti-Fregonara P, Chen K, Liow JS, Fujita M, Innis RB. Image-derived input function for brain PET studies: many challenges and few opportunities. J Cereb Blood Flow Metab. 2011;31:1986–98.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zanotti-Fregonara P, Fadaili EM, Maroy R, Comtat C, Souloumiac A, Jan S, et al. Comparison of eight methods for the estimation of the image-derived input function in dynamic [18F]-FDG PET human brain studies. J Cereb Blood Flow Metab. 2009;29:1825–35.

    Article  PubMed  Google Scholar 

  14. Sari H, Erlandsson K, Law I, Larsson HBW, Ourselin S, Arridge S, et al. Estimation of an image derived input function with MR-defined carotid arteries in FDG-PET human studies using a novel partial volume correction method. J Cereb Blood Flow Metab. 2017;37:1398–409.

    Article  PubMed  Google Scholar 

  15. Sundar LKS, Muzik O, Rischka L, Hahn A, Rausch I, Lanzenberger R, et al. Towards quantitative [18F]FDG-PET/MRI of the brain: automated MR-driven calculation of an image-derived input function for the non-invasive determination of cerebral glucose metabolic rates. J Cereb Blood Flow Metab. 2019;39:1516–30.

    Article  CAS  PubMed  Google Scholar 

  16. Surti S, Pantel AR, Karp JS. Total body PET: why, how, what for? IEEE Trans Radiat Plasma Med Sci IEEE. 2020;4:283–92.

    Article  Google Scholar 

  17. Spencer BA, Berg E, Schmall JP, Omidvari N, Leung EK, Abdelhafez YG, et al. Performance evaluation of the uEXPLORER total-body PET/CT scanner based on NEMA NU 2–2018 with additional tests to characterize PET scanners with a long axial field of view. J Nucl Med. 2021;62:861–70.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Karp JS, Viswanath V, Geagan MJ, Muehllehner G, Pantel AR, Parma MJ, et al. PennPET explorer: design and preliminary performance of a whole-body imager. J Nucl Med. 2020;61:136–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Prenosil GA, Sari H, Fürstner M, Afshar-Oromieh A, Shi K, Rominger A, et al. Performance characteristics of the Biograph Vision Quadra PET/CT system with long axial field of view using the NEMA NU 2–2018 Standard. J Nucl Med. 2021. https://doi.org/10.2967/jnumed.121.261972.

  20. Wang G, Rahmim A, Gunn RN. PET Parametric imaging: past, present, and future. IEEE Trans Radiat Plasma Med Sci. 2020;4:663–75.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Zhang X, Xie Z, Berg E, Judenhofer MS, Liu W, Xu T, et al. Total-body dynamic reconstruction and parametric imaging on the uexplorer. J Nucl Med. 2020;61:285–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Alberts I, Hünermund JN, Prenosil G, Mingels C, Bohn KP, Viscione M, et al. Clinical performance of long axial field of view PET/CT: a head-to-head intra-individual comparison of the Biograph Vision Quadra with the Biograph Vision PET/CT. Eur J Nucl Med Mol Imaging. 2021;48:2395–404.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Liu G, Xu H, Hu P, Tan H, Zhang Y, Yu H, et al. Kinetic metrics of 18F-FDG in normal human organs identified by systematic dynamic total-body positron emission tomography. Eur J Nucl Med Mol Imaging. 2021;48:2363–72.

    Article  CAS  PubMed  Google Scholar 

  24. Liu G, Hu P, Yu H, Tan H, Zhang Y, Yin H, et al. Ultra-low-activity total-body dynamic PET imaging allows equal performance to full-activity PET imaging for investigating kinetic metrics of 18F-FDG in healthy volunteers. Eur J Nucl Med Mol Imaging. 2021;48:2373–83.

    Article  PubMed  Google Scholar 

  25. Viswanath V, Pantel AR, Daube-Witherspoon ME, Doot R, Muzi M, Mankoff DA, et al. Quantifying bias and precision of kinetic parameter estimation on the PennPET Explorer, a long axial field-of-view scanner. IEEE Trans Radiat Plasma Med Sci. 2020;4:735–49.

    Article  PubMed  PubMed Central  Google Scholar 

  26. 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.

  27. 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. Eur J Nucl Med Mol Imaging. 2021;48:21–39.

    Article  PubMed  Google Scholar 

  28. Seifert R, Herrmann K, Kleesiek J, Schäfers M, Shah V, Xu Z, et al. Semiautomatically quantified tumor volume using 68Ga-PSMA-11 PET as a biomarker for survival in patients with advanced prostate cancer. J Nucl Med. 2020;61:1786–92.

    Article  CAS  PubMed  Google Scholar 

  29. Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. Review FSL. Neuroimage. 2012;62:782–90.

    Article  PubMed  Google Scholar 

  30. Wang G, Qi J. Acceleration of the direct reconstruction of linear parametric images using nested algorithms. Phys Med Biol. 2010;55:1505–17.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hu J, Panin V, Smith AM, Spottiswoode B, Shah V, CA von Gall C, et al. Design and implementation of automated clinical whole body parametric PET with continuous bed motion. IEEE Trans Radiat Plasma Med Sci. 2020;4:696–707.

    Article  Google Scholar 

  32. Gallezot J-D, Lu Y, Fontaine K, Mulnix T, Panin V, Hu J, et al. Validation and optimization of direct nested-EM Patlak parametric reconstruction for 18F-FDG using simulated whole-body data. J Nucl Med. 2019;60:453.

  33. Richard Blais A, Lee T-Y. Simulating the effect of venous dispersion on distribution volume measurements from the Logan plot. Biomed Phys Eng Express. 2015;1:045102.

    Article  Google Scholar 

  34. Munk OL, Bass L, Roelgaard K, Bender D, Hansen SB, Keiding S. Liver kinetics of glucose analogs measured in pigs by PET: Importance of dual-input blood sampling. J Nucl Med. 2001;42:795–801.

    CAS  PubMed  Google Scholar 

  35. Iozzo P, Geisler F, Oikonen V, Mäki M, Takala T, Solin O, et al. Insulin stimulates liver glucose uptake in humans: An 18F-FDG PET study. J Nucl Med. 2003;44:682–9.

    CAS  PubMed  Google Scholar 

  36. Holman BF, Cuplov V, Millner L, Hutton BF, Maher TM, Groves AM, et al. Improved correction for the tissue fraction effect in lung PET/CT imaging. Phys Med Biol. 2015;60:7387–402.

    Article  CAS  PubMed  Google Scholar 

  37. Coello C, Fisk M, Mohan D, Wilson FJ, Brown AP, Polkey MI, et al. Quantitative analysis of dynamic 18F-FDG PET/CT for measurement of lung inflammation. EJNMMI Res. 2017. https://doi.org/10.1186/s13550-017-0291-2. 

  38. Lambrou T, Groves AM, Erlandsson K, Screaton N, Endozo R, Win T, et al. The importance of correction for tissue fraction effects in lung PET: Preliminary findings. Eur J Nucl Med Mol Imaging. 2011;38:2238–46.

    Article  PubMed  Google Scholar 

  39. Keramida G, Dunford A, Kaya G, Anagnostopoulos CD, Peters AM. Hepato-splenic axis: hepatic and splenic metabolic activities are linked. Am J Nucl Med Mol Imaging. 2018;8:228–38.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Geist BK, Baltzer P, Fueger B, Hamboeck M, Nakuz T, Papp L, et al. Assessing the kidney function parameters glomerular filtration rate and effective renal plasma flow with dynamic FDG-PET/MRI in healthy subjects. EJNMMI Res. 2018;8:1–9.

    Article  Google Scholar 

  41. Qiao H, Bai J, Chen Y, Tian J. Modeling the excretion of FDG in human kidneys using dynamic PET. Comput Biol Med. 2008;38:1171–6.

    Article  CAS  PubMed  Google Scholar 

  42. Pan L, Cheng C, Haberkorn U, Dimitrakopoulou-Strauss A. Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data. Phys Med Biol IOP Publishing. 2017;62:3566–81.

    Article  CAS  Google Scholar 

  43. Graham MM, Muzi M, Spence AM, O’Sullivan F, Lewellen TK, Link JM, et al. The FDG lumped constant in normal human brain. J Nucl Med. 2002;43:1157–66.

    PubMed  Google Scholar 

  44. Freedman NMT, Sundaram SK, Kurdziel K, Carrasquillo JA, Whatley M, Carson JM, et al. 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.

    Article  PubMed  Google Scholar 

  45. 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. Phys Med Biol. 2013;58:7391–418.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Tseng J, Dunnwald LK, Schubert EK, Link JM, Minoshima S, Muzi M, et al. 18F-FDG kinetics in locally advanced breast cancer: correlation with tumor blood flow and changes in response to neoadjuvant chemotherapy. J Nucl Med. 2004;45:1829–37.

    CAS  PubMed  Google Scholar 

  47. Komar G, Kauhanen S, Liukko K, Seppänen M, Kajander S, Ovaska J, et al. Decreased blood flow with increased metabolic activity: a novel sign of pancreatic tumor aggressiveness. Clin Cancer Res. 2009;15:5511–7.

    Article  CAS  PubMed  Google Scholar 

  48. Mankoff DA, Dunnwald LK, Gralow JR, Ellis GK, Charlop A, Lawton TJ, et al. Blood flow and metabolism in locally advanced breast cancer: Relationship to response to therapy. J Nucl Med. 2002;43:500–9.

    PubMed  Google Scholar 

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Authors and Affiliations

  1. Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland

    Hasan Sari

  2. Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland

    Hasan Sari, Clemens Mingels, Ian Alberts, Dorothee Buesser, Robin Schepers, Patrik Caluori, Ali Afshar-Oromieh, Kuangyu Shi, Axel Rominger & Paul Cumming

  3. Siemens Medical Solutions, USA Inc., Knoxville, TN, USA

    Jicun Hu, Vijay Shah, Vladimir Panin, Maurizio Conti & Lars Eriksson

  4. Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden

    Lars Eriksson

  5. School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia

    Paul Cumming

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  1. Hasan Sari
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Correspondence to Hasan Sari.

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Ethical approval

The local Institutional Review Board approved the study (KEK 2019–02193), and written informed consent was obtained from all patients. The study was performed in accordance with the declaration of Helsinki.

Conflict of interest

HS is a full-time employee of Siemens Healthineers AG. LE, JH, VS, VP, and MC are full-time employees of Siemens Medical Solutions USA, Inc. AR has received research support and speaker honoraria from Siemens Healthineers. No other conflicts of interests were reported.

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Sari, H., Mingels, C., Alberts, I. et al. First results on kinetic modelling and parametric imaging of dynamic 18F-FDG datasets from a long axial FOV PET scanner in oncological patients. Eur J Nucl Med Mol Imaging 49, 1997–2009 (2022). https://doi.org/10.1007/s00259-021-05623-6

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