Abstract
Purpose
Imaging using nuclear medicine is one of the most common procedures in medical centers. Its great advantage is its capacity to analyze the metabolism of the patient, resulting in early diagnosis. However, quantification in nuclear medicine is complicated by many factors, including degradation due to attenuation, scattering, reconstruction algorithms, and assumed models. This project seeks to improve the accuracy and the precision of quantification in PET/CT images.
Methods
We developed a framework, comprising consecutively interlinked steps initiated with the simulation of 3D anthropomorphic phantoms. These phantoms were used to generate realistic PET/CT projections by applying the Geant4 Application for Tomography Emission platform using Monte Carlo simulation. Then, a 3D image reconstruction was created, followed by an Anscombe/Wiener filter and a fuzzy connectedness segmentation process. After defining the region of interest, input activity and response activity curves were generated as excitation functions of the compartment model to enable metabolic quantification of the selected organ or structure. Finally, real PET/CT images provided by the Heart Institute of Hospital das Clínicas, School of Medicine of the University of São Paulo were analyzed using the method.
Results
Metabolic parameters of the three-compartment model based on the MASH anthropomorphic phantom and real PET images were computed for each of the approaches used in this project; the results were similar to the theoretically characteristic values.
Conclusion
The three-dimensional filtering step using the Ascombe/Wiener filter was preponderant and had a high impact on the metabolic quantification process and on other important stages of the whole project.
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References
Anderson DH. Compartmental modeling and tracer kinetics. Dallas. Springer-Verlag Berlin Heidelberg: Department of Mathematics, Southern Methodist University; 1983. https://doi.org/10.1007/978-3-642-51861-4.
Anderson JJA, Mathews D. Site planning and radiation safety in the PET facility. Proceedings of the 44th Annual American Association of Physicists in Medicine; 2002. http://www.aapm.org/meetings/02AM/pdf/8418-39272.pdf Accessed 18 Oct 2020.
Anscombe FJ. The transformation of Poisson, binomial and negative binomial data. Biometrika. 1948;15:246–54. https://doi.org/10.2307/2332343.
Bacharach SL. The new-generation positron emission tomography/computed tomography scanners: implications for cardiac imaging. J Nucl Cardiol. 2004;11:388–92. https://doi.org/10.1016/j.nuclcard.2004.04.008.
Barnett R, Meikle S, Fulton R. Accelerated reconstruction for identifying image regions affected by rigid body movement. 2012 IEEE Nucl Sci Symp and Med Imaging Conf Rec (NSS/MIC). 2012, pp. 3079–3082; https://doi.org/10.1109/NSSMIC.2012.6551702
Baró J, Sempau J, Fernáandez-Varea JM, Salvat F. PENELOPE: an algorithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter. Nucl Inst Methods Phys Res B. 1995;100:31–46. https://doi.org/10.1016/0168-583X(95)00349-5.
Baum KG, Helguera M. Execution of the SimSET Monte Carlo PET/SPECT simulator in the Condor distributed computing environment. J Digit Imaging. 2007 Nov;20(Suppl 1):72–82. https://doi.org/10.1007/s10278-007-9058-z.
Biersack HJ, Briele B, Hotze AL, Oehr P, Liu Q, Mekkawy MA, et al. The role of nuclear medicine in oncology. Ann Nucl Med. 1992 Aug;6(3):131–6. https://doi.org/10.1007/bf03178304.
Blankenberg FG, Strauss HW. Nuclear medicine applications in molecular imaging. J Magn Reson Imaging. 2002 Oct;16(4):352–61. https://doi.org/10.1002/jmri.10171.
Boellaard R, O’Doherty MJ, Weber WA, Mottaghy FM, Lonsdale MN, Stroobants SG, et al. FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0. Eur J Nucl Med Mol Imaging. 2010;37:181–200. https://doi.org/10.1007/s00259-009-1297-4.
Carson RE. Tracer kinetic modeling in PET. Positron emission tomography - basic sciences. London: Springer; 2005. p. 127–59. https://doi.org/10.1007/1-84628-007-9_6.
Carson E, Cobelli C. Modelling methodology for physiology and medicine. Academic Press Series in Biomedical Enginieering; 2001, Chapter 1: 1-13, Chapter 7: 179–208; https://doi.org/10.1016/C2012-0-06031-0.
Carvalho BM, Gau CJ, Herman GT, Kong TY. Algorithms for fuzzy segmentation. Pattern Anal Applic. April 1999;2(1):73–81. https://doi.org/10.1007/s100440050016.
Durkee JW, Streetman JR, Sapir JL, Andrade A. 3-D Monte Carlo and discrete ordinates void coefficient analysis for the Los Alamos National Laboratory Omega West reactor using MCNP and THREEDANT. Prog Nucl Energy. 1999;34(2):99–142. https://doi.org/10.1016/S0149-1970(97)00001-2.
Einicke GA. Iterative filtering and smoothing of measurements possessing Poisson noise. IEEE Trans Aerosp Electron Syst. 2015;51(3). https://doi.org/10.1109/TAES.2015.140843.
Ell PJ. What’s up in nuclear medicine? Cancer Imaging. 2002;2:134. https://doi.org/10.1102/1470-7330.2002.0032.
Falcão AX, Stolfi J, Lotufo RA. The image foresting transform: theory, algorithms, and applications. Pattern analysis and machine intelligence. IEEE Trans Pattern Anal Mach Intell. 2004;26(1):19–29. https://doi.org/10.1109/TPAMI.2004.1261076.
Fatteh UV, Bassan DS. Fuzzy connectedness and its stronger forms. J Math Anal Appl. 1985;111:449–64. https://doi.org/10.1016/0022-247X(85)90229-X.
Gates Medlock KL, Lyne JE, Nock KT. Hypersonic planetary aeroassist simulation system validation. 46th American Institute of Aeronautics and Astronautics (AIAA) Aerospace Sciences Meeting and Exhibit, 2008; https://doi.org/10.2514/6.2008-233
Gonias P, Bertsekas N, Karakatsanis N, Saatsakis G, Gaitanis A, Nikolopoulos D, et al. Validation of a GATE model for the simulation of the Siemens biographTM 6 PET scanner. Nucl Instrum Methods Phys Res A. 2007;571:263–6. https://doi.org/10.1016/j.nima.2006.10.078.
Gonzales RC, Woods RE. Digital image processing. 3rd ed, New Jersey, Pearson Prentice Hall. Chap.9 – Morphological Image Processing; 2008. pp. 519–5666; https://doi.org/10.1117/1.3115362
Hicks RJ, Herman WH, Kalff V, Molina E, Wolfe ER, Hutchins G. Quantitative evolution of regional substrate metabolism in the human heart by positron emission tomography. J Am Coll Cardiol. 1991 Jul;18(1):101–11. https://doi.org/10.1016/s0735-1097(10)80225-6.
Hoffman EJ, Cutler PD, Digby WM, Mazziotta JC. 3D phantom to simulate cerebral blood flow and metabolic images for PET. IEEE Trans Nucl Sci. 1990;37(2):616–20. https://doi.org/10.1109/23.106686.
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 Phys. 1980 Jan;238(1):E69–82. https://doi.org/10.1152/ajpendo.1980.238.1.E69.
Hutton BF. Recent advances in iterative reconstruction for clinical SPECT/PET and CT. Acta Oncol. 2011;50(6):851–8. https://doi.org/10.3109/0284186X.2011.580001.
Inouye T. Square root transform for the analysis of quantum fluctuations in spectrum data. Nucl Inst Methods. 1971;91(4):581–4. https://doi.org/10.1016/0029-554X(71)90682-3.
Jadvar H, Parker JA. Clinical PET and PET/CT: principles and applications. Chapter 2. Springer, 279p. 2010; https://doi.org/10.1007/978-1-4419-0802-5
Jan S, Santin G, Strul D, Staelens S, Assié K, Autret D, et al. GATE: a simulation toolkit for PET and SPECT. Phys Med Biol. 2004;49(19):4543–61. https://doi.org/10.1088/0031-9155/49/19/007.
Jha AK, Purandare NC, Shah S, Agrawal A, Puranik AD, Rangarajan V. PET reconstruction artifact can be minimized by using sinogram correction and filtered back-projection technique. Indian J Radiol Imaging. 2014 Apr-Jun;24(2):103–6. https://doi.org/10.4103/0971-3026.134379.
Johnson JL, Fellows KE, Murphy JD. Transhepatic central venous access for cardiac catheterization and radiologic intervention. Catheter Cardiovasc Diagn. 1995 Jun;35(2):168–71. https://doi.org/10.1002/ccd.1810350219.
Kamasak ME, Bouman CA, Morris ED, Sauer KD. Parametric reconstruction of kinetic PET data with plasma function estimation. Proc. SPIE 5674, Computational Imaging III. Proc. SPIE 5674, Computational Imaging III, 293, Vol. 5674, 2005; https://doi.org/10.1117/12.597630
Kaneta T, Hakamatsuka T, Takanami K, Yamada T, Takase K, Sato A, et al. Evaluation of the relationship between physiological FDG uptake in the heart and age, blood glucose level, fasting period, and hospitalization. Ann Nucl Med. 2006 Apr;20(3):203–8. https://doi.org/10.1007/bf03027431.
Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. Int J Comput Vis. 1988;1:321–31. https://doi.org/10.1007/BF00133570.
Kawrakow I. Accurate condensed history Monte Carlo simulation of electron transport. I. EGSnrc, the new EGS4 version. Med Phys. 2000 Mar;27(3):485–98. https://doi.org/10.1118/1.598917.
King MA, Doherty PW, Schwinger RB. A Wiener filter for nuclear medicine images. Med Phys. 1983;10(6):876–80. https://doi.org/10.1118/1.595352.
Kramer R, Cassola VF, Khoury HJ, Vieira JW, de Melo LVJ, Brown KR. FASH and MASH: female and male adult human phantoms based on polygon mesh surfaces: II. Dosimetric Calculations. Phys Med Biol. 2010 Jan 7;55(1):163–89. https://doi.org/10.1088/0031-9155/55/1/010.
Lee JS. Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell. 1980;PAMI-2:165–8. https://doi.org/10.1109/TPAMI.1980.4766994.
Ljungberg M, Larsson A, Johansson L. A new collimator simulation in SIMIND based on the delta-scattering technique. IEEE Trans Nucl Sci. October 2005;52(5):1370–5. https://doi.org/10.1109/TNS.2005.858252.
Ljungberg M, Strand SE, King MA. Monte Carlo calculations in nuclear medicine: applications in diagnostic imaging. 2nd ed. Lund. Series in medical physics and biomedical engineering: Medical Radiation Physics; 2012. http://books.google.se/books?isbn=1439841098
Maddahi J, Packard RRS. Cardiac PET perfusion tracers: current status and future directions. Semin Nucl Med. 2014;44(5):333–43. https://doi.org/10.1053/j.semnuclmed.2014.06.011.
Maldonado GI, Xoubi N, Zhao Z. Enhancement of a subcritical experimental facility via MCNP simulations. Ann Nucl Energy. 2008;35:263–8. https://doi.org/10.1016/j.anucene.2007.06.022.
Mascarenhas NDA, Santos CAN, Cruvinel PE. Transmition tomography under Poisson noise using the Anscombe transform and a Wiener filter of the projections. Nucl Instrum Methods Phys Res A. 1999;423:265–71. https://doi.org/10.1016/S0168-9002(98)00925-5.
Matis JH, Wehrly TE, Gerald KB. Tracer kinetics and physiologic modeling: theory to practice. College Station. Springer-Verlag Berlin Heidelberg: Texas A&M Univeristy; 1983. https://doi.org/10.1007/978-3-642-50036-7.
McCarthy TJ, Schwarz SW, Welch MJ. Nuclear medicine and positron emission tomography: an overview. J Chem Educ. 1994;71(10):830. https://doi.org/10.1021/ed071p830.
McQuaid S, Hutton B. Sources of attenuation-correction artefacts in cardiac PET/CT and SPECT/CT. Eur J Nucl Med Mol Imaging. 2008;35:1117–23. https://doi.org/10.1007/s00259-008-0718-0.
Megasan E, Puzzuoli D, Granade CE, Cory DG. Modeling quantum noise for efficient testing of fault-tolerant circuits. Phys Rev A. 2013;87:012324. https://doi.org/10.1103/PhysRevA.87.012324.
Mettler FA, Huda W, Yoshizumi TT, Mahesh M. Effective doses in radiology and diagnostic nuclear medicine: a catalog. Radiology. 2008;248:254–63. https://doi.org/10.1148/radiol.2481071451.
Morita K, Katoh C, Yoshinaga K, Noriyasu K, Mabuchi M, Tsukamoto T, et al. Quantitative analysis of myocardical glucose utilization in patients with left ventricular dysfunction by means of 18F−FDG dynamic positron tomography and three-compartment analysis. Eur J Nucl Med Mol Imaging. 2005;32(7):806–12. https://doi.org/10.1007/s00259-004-1743-2.
Muzic R, Cornelius S. COMKAT: compartment model kinetic analysis tool. J Nucl Med. 2001 Apr;42(4):636–45.
Noori-Asl M, Sadremomtaz A. Analytical image reconstruction methods in emission tomography. J Biomed Sci Eng. 2013;6:100–7. https://doi.org/10.4236/jbise.2013.61013.
Nyúl ALG, Falcão AX, Udupa JK. Fuzzy-connected 3D image segmentation at interactive speeds. Graph Model. 2002;64:259–81. https://doi.org/10.1016/S1077-3169(02)00005-9.
O’Neil D. Blood components. Palomar College; 1999. https://www2.palomar.edu/anthro/blood/blood_components.htm Accessed 18 Oct 2020.
Osher S, Fedkiw R. Level set methods and dynamic implicit surfaces. New York: Springer-Verlag; 2002. https://doi.org/10.1007/b98879.
Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern Syst. 1979;9(1):62–6. https://doi.org/10.1109/tsmc.1979.4310076.
Paydary K, Seraj SM, Zadeh MZ, Emamzadehfard S, Shamchi SP, Gholami S, et al. The evolving role of FDG-PET/CT in the diagnosis, staging, and treatment of breast cancer. Mol Imaging Biol. 2019 Feb;21(1):1–10. https://doi.org/10.1007/s11307-018-1181-3.
Pednekar AS, Kakadiaris IA. Image segmentation based on fuzzy connectedness using dynamic weights. IEEE Trans Image Process. 2006;15(6). https://doi.org/10.1109/TIP.2006.871165.
Phelps ME. PET: molecular imaging and its biological. New York: Springer-Verlag; 2004. https://doi.org/10.1007/978-0-387-22529-6.
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 Nov;6(5):371–88. https://doi.org/10.1002/ana.410060502.
Rajagopalan V, Pioro E. Comparing brain structural MRI and metabolic FDGPET changes in patients with ALS-FTD: ‘the chicken or the egg?’ question. J Neurol Neurosurg Psychiatry. 2015 Sep;86(9):952–8. https://doi.org/10.1136/jnnp-2014-308239.
Reivich M, Kuhl D, Wolf A. Measurement of local cerebral glucose metabolism in man with 18F-2-fluoro-2-deoxy-d-glucose. Acta Neurol Scand Suppl. 1977;64:190–1.
Reivich M, Alavi A, Wolf A, Fowler J, Russell J, Arnett C, et al. Glucose metabolic rate kinetic model parameter determination in humans: the lumped constant and rate constants for (18F)fluorodeoxyglucose and (11C)deoxyglucose. J Cereb Blood Flow Metab. 1985 Jun;5(2):179–92. https://doi.org/10.1038/jcbfm.1985.24.
Robertson JS. Computer applications in nuclear medicine. J Chronic Dis. 1966 Apr;19(4):443–59. https://doi.org/10.1016/0021-9681(66)90119-6.
Schelbert HR, Hoh CK, Royal HD, Brown M, Dahlbom MN, Dehdashti F, et al. Procedure guideline for tumor imaging using F-18 FDG. J Nucl Med. 1998 Jul;39(7):1302–5.
Schmidtlein CR, Kirov AS, Nehmeh SA, Erdi YE, Humm JL, Amols HI, Bidaut LM, Ganin A, Stearns CW, McDaniel DL, Hamacher KA. Validation of GATE Monte Carlo simulations of the GE Advance/Discovery LS PET scanners. Medical Physics. 2006;33(1):198–208
Schuemann J. Monte Carlo calculations in nuclear medicine, second edition: applications in diagnostic imaging. Med Phys. 2014;41:047302. https://doi.org/10.1118/1.4869177.
Selberg K, Ross M. Advances in nuclear medicine. Vet Clin Equine. 2012;28:527–38. https://doi.org/10.1016/j.cveq.2012.09.004.
Sergars WP, Tsui BMW. MCAT TO XCAT: the evolution of 4-D computerized phantoms for imaging research. IEEE. 2009;97(12). https://doi.org/10.1109/JPROC.2009.2022417.
Silva JEMM, Furuie SS. Adequacy of compartmental model for positron emission tomography examinations. Rev Brasil Engenharia Biomed. 2011;27(4):231–42. https://doi.org/10.4322/rbeb.2011.019.
Simoncic U, Jeraj R. Cumulative input function method for linear compartmental models and spectral analysis in PET. J Cereb Blood Flow Metab. 2011;31:750–6. https://doi.org/10.1038/jcbfm.2010.159.
Sokoloff L. [1-14C]-2-deoxy-d-glucose method for measuring local cerebral glucose utilization. Mathematical analysis and determination of the “lumped” constants. Neurosci Res Program Bull. 1976;14:466–8.
Sokoloff L, Reivich M, Kennedy C. The [14C] deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and anesthetized albino rat. J Neurochem. 1977;28:897–916. https://doi.org/10.1111/j.1471-4159.1977.tb10649.x.
Storch-Becker A, Kaiser KP, Feinendegen LE. Cardiac nuclear medicine: positron emission tomography in clinical medicine. Eur J Nucl Med. 1988;13(12):648–52. https://doi.org/10.1007/BF00256392.
Tsui BMW, Frey EC. Analytic image reconstruction methods in emission computed tomography. In: Zaidi H, editor. Quantitative analysis in nuclear medicine imaging. Boston: Springer; 2006. https://doi.org/10.1007/0-387-25444-7_3.
Udupa JK, Saha PK. Fuzzy connectedness and image segmentation. Proced IEEE. October 2003;91:1649–69. https://doi.org/10.1109/JPROC.2003.817883.
Udupa JK, Samarasekera S. Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graph Models Imag Process. 1996;58(3):246–61. https://doi.org/10.1006/gmip.1996.0021.
Wang CX, Snyder WE, Bilbro G, Santago P. Performance evaluation of filtered backprojection reconstruction and iterative reconstruction methods for PET images. Comput Biol Med. January 1998;28(1):13–25. https://doi.org/10.1016/S0010-4825(97)00031-0.
Worsley KJ. An overview and some new developments in the statistical analysis of PET and fMRI data. Hum Brain Mapp. 1997;5:254–8. https://doi.org/10.1002/(SICI)1097-0193(1997)5:4%3C254::AID-HBM9%3E3.0.CO;2-2.
Xu JP. Generalized gradient vector flow external forces for active contours. Signal Process. 1998;71(2):131–9. https://doi.org/10.1016/s0165-1684(98)00140-6.
Zaidi H, Hasegawa B. Determination of the attenuation map in emission tomography. J Nucl Med. 2003;44:291–315.
Zeng GL. A filtered backprojection algorithm with characteristics of the iterative Landweber algorithm. Med Phys. 2012 Feb;39(2):603–7. https://doi.org/10.1118/1.3673956.
Zoccarato O, Scabbio C, De Ponti E, Matheoud R, Leva L, Morzenti S, et al. Comparative analysis of iterative reconstruction algorithms with resolution recovery for cardiac SPECT studies. A multi-center phantom study. J Nucl Cardiol. 2013;21(1):135–48. https://doi.org/10.1007/s12350-013-9821-0.
Acknowledgments
The authors wish to thank the Instituto do Coração (InCor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HC-FMUSP), for providing the PET/CT images used in this study.
Funding
This study was funded by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), with grant numbers 2011/23172-6 and 2014/11758-4.
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This research study was conducted retrospectively using PET/CT images provided by the Instituto do Coração (InCor), which have the approval of the Ethics Committee of the Hospital das Clínicas, School of Medicine of the University of São Paulo (HC-FMUSP), CONEP No. 16814.
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Florez, E., Vijayakumar, V. & Shiguemi Furuie, S. Dynamic and metabolic quantification of nuclear medicine images in the PET/CT modality. Res. Biomed. Eng. 37, 299–318 (2021). https://doi.org/10.1007/s42600-020-00117-0
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DOI: https://doi.org/10.1007/s42600-020-00117-0