핵의학 영상과 추적자 동력학 분석법을 이용한 생체기능 정량화

Quantitation of In-Vivo Physiological Function using Nuclear Medicine Imaging and Tracer Kinetic Analysis Methods

  • 김수진 (서울대학교 의과대학 핵의학교실) ;
  • 김경민 (한국원자력의학원 방사선의학연구소 분자영상연구부) ;
  • 이재성 (서울대학교 의과대학 핵의학교실)
  • Kim, Su-Jin (Department of Nuclear Medicine, College of Medicine, Seoul National University) ;
  • Kim, Kyeong-Min (Molecular Imaging Research Center, Korea Institute of Radiological and Medical Sciences) ;
  • Lee, Jae-Sung (Department of Nuclear Medicine, College of Medicine, Seoul National University)
  • 발행 : 2008.04.30

초록

Nuclear medicine imaging has an unique advantage of absolute quantitation of radioactivity concentration in body. Tracer kinetic analysis has been known as an useful investigation methods in quantitative study of in-vivo physiological function. The use of nuclear medicine imaging and kinetic analysis together can provide more useful and powerful intuition in understanding biochemical and molecular phenomena in body. There have been many development and improvement in kinetic analysis methodologies, but the conventional basic concept of kinetic analysis is still essential and required for further advanced study using new radiopharmaceuticals and hybrid molecular imaging techniques. In this paper, the basic theory of kinetic analysis and imaging techniques for suppressing noise were summarized.

키워드

참고문헌

  1. Kety SS. Measurement of local blood flow by the exchange of an inert, diffusible substance. Methods Mes Res 1960;8:228-36
  2. Iida H, Kanno I, Takahashi A, et al.. Measurement of absolute myocardial blood flow with $H_2\;^{15}O$ and dynamic positron- emission tomography. Strategy for quantification in relation to the partial-volume effect. Circulation 1988;78:104-15 https://doi.org/10.1161/01.CIR.78.1.104
  3. Iida H, Law I, Pakkenberg B, et al. Quantitation of regional cerebral blood flow corrected for partial volume effect using O-15 water and PET: I. Theory, error analysis, and stereologic comparison. J Cereb Blood Flow Metab 2000;20:1237-51 https://doi.org/10.1097/00004647-200008000-00009
  4. Iida H, Kanno I, Miura S, et al. A determination of the regional brain/blood partition coefficient of water using dynamic positron emission tomography. J Cereb Blood Flow Metab 1989;9:874-85 https://doi.org/10.1038/jcbfm.1989.121
  5. Iida H, Akutsu T, Endo K, et al. A multicenter validation of regional cerebral blood flow quantitation using [$^{123}I$]iodoamphetamine and single photon emission computed tomography. J Cereb Blood Flow Metab 1996;16:781-93 https://doi.org/10.1097/00004647-199609000-00003
  6. Takeuchi R, Matsuda H, Yonekura Y, et al. Noninvasive quantitative measurements of regional cerebral blood flow using technetium-99m- L,L-ECD SPECT activated with acetazolamide: quantification analysis by equal-volume-split $^{99m}$Tc-ECD consecutive SPECT method. J Cereb Blood Flow Metab 1997;17:1020-32 https://doi.org/10.1097/00004647-199710000-00003
  7. Kim KM, Watabe H, Hayashi T, et al. Quantitative mapping of basal and vasareactive cerebral blood flow using split-dose $^{123}I$-iodoamphetamine and single photon emission computed tomography. Neuroimage. 2006;33:1126-35 https://doi.org/10.1016/j.neuroimage.2006.06.064
  8. Gjedde A, Diemer NH. Autoradiographic determination of regional brain glucose content. J Cereb Blood Flow Metab 1983;3:303-10 https://doi.org/10.1038/jcbfm.1983.45
  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 https://doi.org/10.1038/jcbfm.1983.1
  10. Logan J, Fowler JS, Volkow ND, et al. Graphical analysis of reversible radioligand binding from time-activity measurements applied to [N-$^{11}C$-methyl]-(-)-cocaine PET studies in human subjects. J Cereb Blood Flow Metab 1990;10:740-7 https://doi.org/10.1038/jcbfm.1990.127
  11. Logan J, Fowler JS, Volkow ND, Wang GJ, Ding YS, Alexoff DL. Distribution volume ratios without blood sampling from graphical analysis of PET data. J Cereb Blood Flow Metab 1996;16:834-40 https://doi.org/10.1097/00004647-199609000-00008
  12. Carson RE. PET parameter estimation using linear intergration methods: bias and variability consideration. In: quantification of brain function: tracer kinetics and image analysis in brain PET.: Amsterdam: Elsevier Science Publishers; 1993.p. 499-507
  13. Slifstein M, Laruelle M. Effects of statistical noise on graphic analysis of PET neuroreceptor studies. J Nucl Med 2000;41: 2083-8
  14. Logan J, Fowler JS, Volkow ND, Ding YS, Wang GJ, Alexoff DL. A strategy for removing the bias in the graphical analysis method. J Cereb Blood Flow Metab 2001;21:307-20 https://doi.org/10.1097/00004647-200103000-00014
  15. Gunn RN, Lammertsma AA, Hume SP, Cunningham VJ. Parametric imaging of ligand-receptor binding in PET using a simplified reference region model. Neuroimage 1997;6: 279-87 https://doi.org/10.1006/nimg.1997.0303
  16. Lammertsma AA, Hume SP. Simplified reference tissue model for PET receptor studies. Neuroimage 1996;4:153-8 https://doi.org/10.1006/nimg.1996.0066
  17. Cselenyi Z, Olsson H, Halldin C, Gulyas B, Farde L. A comparison of recent parametric neuroreceptor mapping approaches based on measurements with the high affinity PET radioligands [$^{11}C$]FLB 457 and [11]WAY 100635. Neuroimage 2006;32:1690-708 https://doi.org/10.1016/j.neuroimage.2006.02.053
  18. Ichise M, Toyama H, Innis RB, Carson RE. Strategies to improve neuroreceptor parameter estimation by linear regression analysis. J Cereb Blood Flow Metab 2002;22:1271-81 https://doi.org/10.1097/01.WCB.0000038000.34930.4E
  19. Joshi A, Fessler JA, Koeppe RA. Improving PET receptor binding estimates from Logan plots using principal component analysis. J Cereb Blood Flow Metab 2008;28:852-65 https://doi.org/10.1038/sj.jcbfm.9600584
  20. Feng D, Sung-Cheng H, ZhiZhong W, Ho DAHD. An unbiased parametric imaging algorithm for nonuniformly sampled biomedical system parameter estimation. IEEE Transactions on Medical Imaging 1996;15:512-8 https://doi.org/10.1109/42.511754
  21. Varga J, Szabo Z. Modified regression model for the Logan plot. J Cereb Blood Flow Metab 2002;22:240-4 https://doi.org/10.1097/00004647-200202000-00012
  22. Faraway J. Linear Models with R: CRC Press; 2004. p.131-7
  23. Barber DC. The use of principal components in the quantitative analysis of gamma camera dynamic studies. Phys Med Biol 1980;25:283-92 https://doi.org/10.1088/0031-9155/25/2/008
  24. Pedersen F, Bergstrom M, Bengtsson E, Langstrom B. Principal component analysis of dynamic positron emission tomography images. Eur J Nucl Med 1994;21:1285-92 https://doi.org/10.1007/BF02426691
  25. Thireou T, Strauss LG, Dimitrakopoulou-Strauss A, Kontaxakis G, Pavlopoulos S, Santos A. Performance evaluation of principal component analysis in dynamic FDG-PET studies of recurrent colorectal cancer. Comput Med Imaging Graph 2003;27:43-51 https://doi.org/10.1016/S0895-6111(02)00050-2
  26. Razifar P, Axelsson J, Schneider H, Langstrom B, Bengtsson E, Bergstrom M. A new application of pre-normalized principal component analysis for improvement of image quality and clinical diagnosis in human brain PET studies--clinical brain studies using [$^{11}C$]-GR205171, [$^{11}C$]-L-deuterium-deprenyl, [$^{11}C$]-5-Hydroxy-L-Tryptophan, [$^{11}C$]-LDOPA and Pittsburgh Compound-B. Neuroimage 2006;33: 588-98 https://doi.org/10.1016/j.neuroimage.2006.05.060
  27. Kimura Y, Senda M, Alpert NM. Fast formation of statistically reliable FDG parametric images based on clustering and principal components. Phys Med Biol 2002;47: 455-68 https://doi.org/10.1088/0031-9155/47/3/307
  28. Millet P, Ibanez V, Delforge J, Pappata S, Guimon J. Wavelet analysis of dynamic PET data: application to the parametric imaging of benzodiazepine receptor concentration. Neuroimage 2000;11:458-72 https://doi.org/10.1006/nimg.2000.0563