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Tracer kinetic modelling of tumour angiogenesis based on dynamic contrast-enhanced CT and MRI measurements

  • Gunnar Brix
  • Jürgen Griebel
  • Fabian Kiessling
  • Frederik Wenz
Article

Abstract

Purpose

Technical developments in both magnetic resonance imaging (MRI) and computed tomography (CT) have helped to reduce scan times and expedited the development of dynamic contrast-enhanced (DCE) imaging techniques. Since the temporal change of the image signal following the administration of a diffusible, extracellular contrast agent (CA) is related to the local blood supply and the extravasation of the CA into the interstitial space, DCE imaging can be used to assess tissue microvasculature and microcirculation. It is the aim of this review to summarize the biophysical and tracer kinetic principles underlying this emerging imaging technique offering great potential for non-invasive characterization of tumour angiogenesis.

Methods

In the first part, the relevant contrast mechanisms are presented that form the basis to relate signal variations measured by serial CT and MRI to local tissue concentrations of the administered CA. In the second part, the concepts most widely used for tracer kinetic modelling of concentration-time courses derived from measured DCE image data sets are described in a consistent and unified manner to highlight their particular structure and assumptions as well as the relationships among them. Finally, the concepts presented are exemplified by the analysis of representative DCE data as well as discussed with respect to present and future applications in cancer diagnosis and therapy.

Results

Depending on the specific protocol used for the acquisition of DCE image data and the particular model applied for tracer kinetic analysis of the derived concentration-time courses, different aspects of tumour angiogenesis can be quantified in terms of well-defined physiological tissue parameters.

Conclusions

DCE imaging offers promising prospects for improved tumour diagnosis, individualization of cancer treatment as well as the evaluation of novel therapeutic concepts in preclinical and early-stage clinical trials.

Keywords

Contrast-enhanced dynamic imaging Microcirculation Microvasculature Indicator dilution theory Compartmental modelling 

Notes

Acknowledgement

This work was supported in part by the German ‘Competence Alliance on Radiation Research’ (BMBF 03NUK008F). We thank G. Hellwig for helpful discussions on mathematical aspects as well M. Salehi Ravesh and S. Zwick for technical support.

Conflicts of interest

None.

References

  1. 1.
    Folkman J. Tumor angiogenesis: therapeutic implications. N Engl J Med 1971;285:1182–6.CrossRefPubMedGoogle Scholar
  2. 2.
    Folkman J. Anti-angiogenesis: new concept for therapy of solid tumors. Ann Surg 1972;175:409–16.CrossRefPubMedGoogle Scholar
  3. 3.
    Carmeliet P, Jain RK. Angiogenesis in cancer and other diseases. Nature 2000;407:249–57.CrossRefPubMedGoogle Scholar
  4. 4.
    Bassingthwaighte JB, Chinard FP, Crone C, Goresky CC, Lassen NA, Reneman RS, et al. Terminology for mass transport and exchange. Am J Physiol 1986;250:H539–45.PubMedGoogle Scholar
  5. 5.
    Rescigno A, Thakur AK, Brill AB, Mariani G. Tracer kinetics: a proposal for unified symbols and nomenclature. Phys Med Biol 1990;35:449–65.CrossRefGoogle Scholar
  6. 6.
    Bassingthwaighte JB, Goresky CA. Modeling in the analysis of solute and water exchange in the microvasculature. In: Renkin EM, Michel CC, Geiger SR, editors. Handbook of physiology. Section 2. The cardiovascular system. Bethesda: American Physiological Society; 1984. p. 549–626.Google Scholar
  7. 7.
    King RB, Raymond GM, Bassingthwaighte JB. Modeling blood flow heterogeneity. Ann Biomed Eng 1996;24:352–72.CrossRefPubMedGoogle Scholar
  8. 8.
    Griess H. Chemistry of X-ray contrast agents. In: Dawson P, Cosgrove DO, Grainger RG, editors. Textbook of contrast media. Oxford: Isis Medical Media; 1999. p. 15–22.Google Scholar
  9. 9.
    Bushberg JT, Seibert JA, Leidholdt EM, Boone JM. The essential physics of medical imaging. 2nd ed. Philadelphia: Lippincott Williams & Wilkins; 2002.Google Scholar
  10. 10.
    Dawson P. Gadolinium chelates: chemistry. In: Dawson P, Cosgrove DO, Grainger RG, editors. Textbook of contrast media. Oxford: Isis Medical Media; 1999. p. 291–318.Google Scholar
  11. 11.
    Morgan LO, Nolle AW. Proton spin relaxation in aqueous solutions of paramagnetic ions. II. Cr+++, Mn++, Ni++, Cu++, and Gd+++. J Chem Phys 1959;31:365–8.CrossRefGoogle Scholar
  12. 12.
    Gadian DG, Payne JA, Bryant DJ, Young IR, Carr DH, Bydder GM. Gadolinium-DTPA as a contrast agent in MR imaging—theoretical projections and practical observations. J Comput Assist Tomogr 1985;9:242–51.CrossRefPubMedGoogle Scholar
  13. 13.
    Strich G, Hagan PL, Gerber KH, Slutsky RA. Tissue distribution and magnetic resonance spin lattice relaxation effects of gadolinium-DTPA. Radiology 1985;154:723–6.PubMedGoogle Scholar
  14. 14.
    Koenig SH, Spiller M, Brown RD, Wolf GL. Relaxation of water protons in the intra- and extracellular regions of blood containing Gd(DTPA). Magn Reson Med 1986;3:791–5.CrossRefPubMedGoogle Scholar
  15. 15.
    Donahue KM, Weisskoff RM, Burstein D. Water diffusion and exchange as they influence contrast enhancement. J Magn Reson Imaging 1997;7:102–10.CrossRefPubMedGoogle Scholar
  16. 16.
    Villringer A, Rosen BR, Belliveau JW, Ackerman JL, Lauffer RB, Buxton RB, et al. Dynamic imaging with lanthanide chelates in normal brain: contrast due to magnetic susceptibility effects. Magn Reson Med 1988;6:164–74.CrossRefPubMedGoogle Scholar
  17. 17.
    Rosen BR, Belliveau JW, Chien D. Perfusion imaging by nuclear magnetic resonance. Magn Reson Q 1989;5:263–81.PubMedGoogle Scholar
  18. 18.
    Nekolla S, Gneiting T, Syha J, Deichmann R, Haase A. T1 maps by K-space reduced snapshot-FLASH MRI. J Comput Assist Tomogr 1992;16:327–32.CrossRefPubMedGoogle Scholar
  19. 19.
    Hoffmann U, Brix G, Knopp MV, Heß T, Lorenz WJ. Pharmacokinetic mapping of the breast: a new method for dynamic MR mammography. Magn Reson Med 1995;33:506–14.CrossRefPubMedGoogle Scholar
  20. 20.
    Stepanow B, Blüml S, Brix G. Comparison of T1 measurements by means of TurboFLASH techniques performed on a conventional whole-body MR imager. SMRM, 12th Scientific Meeting, Book of Abstracts 1993;2:742.Google Scholar
  21. 21.
    Brix G, Kiessling F, Lucht R, Darai S, Wasser K, Delorme S, et al. Microcirculation and microvasculature in breast tumors: pharmacokinetic analysis of dynamic MR image series. Magn Reson Med 2004;52:420–9.CrossRefPubMedGoogle Scholar
  22. 22.
    Fisel CR, Ackerman JL, Buxton RB, Garrido L, Belliveau JW, Rosen BR, et al. MR contrast due to microscopically heterogeneous magnetic susceptibility: numerical simulations and applications to cerebral physiology. Magn Reson Med 1991;17:336–47.CrossRefPubMedGoogle Scholar
  23. 23.
    Meier P, Zierler KL. On the theory of the indicator-dilution method for measurement of blood flow and volume. J Appl Physiol 1954;6:731–44.PubMedGoogle Scholar
  24. 24.
    Zierler KL. Theoretical basis of indicator-dilution methods for measuring flow and volume. Circ Res 1962;10:393–407.Google Scholar
  25. 25.
    Zierler KL. Theory of use of indicators to measure blood flow and extracellular volume and calculation of transcapillary movement of tracers. Circ Res 1963;12:464–71.Google Scholar
  26. 26.
    Zierler KL. Equations for measuring blood flow by external monitoring of radioisotopes. Circ Res 1965;16:309–21.PubMedGoogle Scholar
  27. 27.
    Rempp K, Brix G, Wenz F, Becker C, Gückel F, Lorenz WJ. Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging. Radiology 1994;193:637–41.PubMedGoogle Scholar
  28. 28.
    Stritzke P, King MA, Vaknine R, Goldsmith SJ. Deconvolution using orthogonal polynomials in nuclear medicine: a method for forming quantitative functional images from kinetic studies. IEEE Trans Med Imaging 1990;9:11–23.CrossRefPubMedGoogle Scholar
  29. 29.
    Schreiber W, Gückel F, Stritzke P, Schmiedek P, Schwarz A, Brix G. Cerebral blood flow and cerebrovascular reserve capacity: estimation by dynamic magnetic resonance imaging. J Cereb Blood Flow Metab 1998;18:1143–56.CrossRefPubMedGoogle Scholar
  30. 30.
    Østergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn Reson Med 1996;36:715–25.CrossRefPubMedGoogle Scholar
  31. 31.
    Calamante F, Gadian DG, Connelly A. Quantification of bolus-tracking MRI: improved characterization of the tissue residue function using Tikhonov regularization. Magn Reson Med 2003;50:1237–47.CrossRefPubMedGoogle Scholar
  32. 32.
    Griebel J, Pahernik S, Lucht R, DeVries R, Englmeier KH, Dellian M, Brix G. Perfusion and permeability: can both parameters be evaluated separately from dynamic MR data. Proc Int Soc Magn Reson Med 2001:629.Google Scholar
  33. 33.
    Wu O, Østergaard L, Koroshetz WJ, Schwamm LE, O’Donnell J, Schaefer PW, et al. Effects of tracer arrival time on flow estimates in MR perfusion-weighted imaging. Magn Reson Med 2003;50:856–64.CrossRefPubMedGoogle Scholar
  34. 34.
    Miles KA. Measurement of tissue perfusion by dynamic computed tomography. Br J Radiol 1991;64:409–12.CrossRefPubMedGoogle Scholar
  35. 35.
    Brix G, Zwick S, Griebel J, Fink C, Kiessling F. Estimation of tissue perfusion by dynamic contrast-enhanced imaging: simulation-based evaluation of the steepest-slope method. Eur Radiolody, doi: 10.1007/s00330-010-1787-6.
  36. 36.
    Dawson P. Functional and physiological imaging. In: Dawson P, Cosgrove DO, Grainger RG, editors. Textbook of contrast media. Oxford: Isis Medical Media; 1999. p. 75–93.Google Scholar
  37. 37.
    Blomley MJK, Coulden R, Bufkin C, Lipton MJ, Dawson P. Contrast bolus dynamic computed tomography for the measurement of solid organ perfusion. Invest Radiol 1993;28:S72–7.CrossRefPubMedGoogle Scholar
  38. 38.
    Cobelli C, Foster D, Toffolo G. Tracer kinetics in biomedical research. From data to model. New York: Kluwer Academic/Plenum Publishers; 2000.Google Scholar
  39. 39.
    Morales MF, Smith RE. On the theory of blood-tissue exchange of inert gases. VI. Validity of approximate uptake expressions. Bull Math Biophys 1948;10:191–200.CrossRefPubMedGoogle Scholar
  40. 40.
    Brix G, Bahner M, Hoffmann U, Horvath A, Schreiber W. Regional blood flow, capillary permeability, and compartment volumes: measurement with dynamic computed tomography—initial experience. Radiology 1999;210:269–76.PubMedGoogle Scholar
  41. 41.
    Brix G, Zwick S, Kiessling F, Griebel J. Pharmacokinetic analysis of tissue microcirculation using nested models: multimodel inference and parameter identifiability. Med Phys 2009;36:2923–33.CrossRefPubMedGoogle Scholar
  42. 42.
    Peters AM, Myers MJ. Physiological measurements with radionuclides in clinical practice. Oxford: Oxford University Press; 1998.Google Scholar
  43. 43.
    Kety SS. The theory and applications of the exchange of inert gas at the lungs and tissues. Pharmacol Rev 1951;3:1–41.PubMedGoogle Scholar
  44. 44.
    Renkin EM. Transport of potassium-42 from blood to tissue in isolated mammalian skeletal muscles. Am J Physiol 1959;197:1205–10.PubMedGoogle Scholar
  45. 45.
    Crone C. The permeability of capillaries in various organs as determined by use of the ‘indicator diffusion’ method. Acta Physiol Scand 1963;58:292–305.CrossRefPubMedGoogle Scholar
  46. 46.
    Tofts PS. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging 1997;7:91–101.CrossRefPubMedGoogle Scholar
  47. 47.
    Tofts PA, Kermode AG. Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn Reson Med 1991;17:357–67.CrossRefPubMedGoogle Scholar
  48. 48.
    Brix G, Semmler W, Port R, Schad LR, Layer G, Lorenz WJ. Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR imaging. J Comput Assist Tomogr 1991;15:621–8.CrossRefPubMedGoogle Scholar
  49. 49.
    Zwick S, Brix G, Tofts PS, Strecker R, Kopp-Schneider A, Laue H, et al. Simulation-based comparison of two approaches frequently used for dynamic contrast-enhanced MRI. Eur Radiol 2010;20:432–42.CrossRefPubMedGoogle Scholar
  50. 50.
    Burnham KP, Anderson DR. Model selection and multimodel inference. A practical information-theoretical approach. 2nd ed. New York: Springer; 2002.Google Scholar
  51. 51.
    Buckley DL. Uncertainty in the analysis of tracer kinetics using dynamic contrast-enhanced T1-weighted MRI. Magn Reson Med 2002;47:601–6.CrossRefPubMedGoogle Scholar
  52. 52.
    Johnson JA, Wilson TA. A model for capillary exchange. Am J Physiol 1966;210:1299–303.PubMedGoogle Scholar
  53. 53.
    St Lawrence KS, Lee TY. An adiabatic approximation to the tissue homogeneity model for water exchange in the brain: I. Theoretical derivation. J Cereb Blood Flow Metab 1998;18:1365–77.CrossRefPubMedGoogle Scholar
  54. 54.
    Vaupel P, Höckel M. Blood supply, oxygenation status and metabolic micromilieu of breast cancer: characterization and therapeutic relevance. Int J Oncol 2000;17:869–79.PubMedGoogle Scholar
  55. 55.
    Vaupel P. Blood flow and oxygenation status of head and neck carcinomas. Adv Exp Med Biol 1997;428:89–95.PubMedGoogle Scholar
  56. 56.
    Warner E, Messersmith H, Causer P, Eisen A, Shumak R, Plewes D. Systematic review: using magnetic resonance imaging to screen women at high risk for breast cancer. Ann Intern Med 2008;148:671–9.PubMedGoogle Scholar
  57. 57.
    Turnbull LW. Dynamic contrast-enhanced MRI in the diagnosis and management of breast cancer. NMR Biomed 2009;22:28–39.CrossRefPubMedGoogle Scholar
  58. 58.
    Kurhanewicz J, Vigneron D, Carroll P, Coakley F. Multiparametric magnetic resonance imaging in prostate cancer: present and future. Curr Opin Urol 2008;18:71–7.CrossRefPubMedGoogle Scholar
  59. 59.
    Hawighorst H, Knapstein PG, Knopp MV, Weikel W, Brix G, Zuna I, et al. Uterine cervical carcinoma: comparison of standard and pharmacokinetic analysis of time-intensity curves for assessment of tumor angiogenesis and patient survival. Cancer Res 1998;58:3598–602.PubMedGoogle Scholar
  60. 60.
    Mazaheri Y, Shukla-Dave A, Hricak H, Fine SW, Zhang J, Inurrigarro G, et al. Prostate cancer: identification with combined diffusion-weighted MR imaging and 3D 1H MR spectroscopic imaging—correlation with pathologic findings. Radiology 2008;246:480–8.CrossRefPubMedGoogle Scholar
  61. 61.
    Lim HK, Kim JK, Kim KA. Prostate cancer: apparent diffusion coefficient map with T2-weighted images for detection—a multireader study. Radiology 2009;250:145–51.CrossRefPubMedGoogle Scholar
  62. 62.
    Padhani AR. Dynamic contrast-enhanced MRI in clinical oncology: current status and future directions. J Magn Reson Imaging 2002;16:407–22.CrossRefPubMedGoogle Scholar
  63. 63.
    Hillengass J, Wasser K, Delorme S, Kiessling F, Zechmann C, Benner A, et al. Lumbar bone marrow microcirculation measurements from dynamic contrast-enhanced magnetic resonance imaging is a predictor of event-free survival in progressive multiple myeloma. Clin Cancer Res 2007;13:475–81.CrossRefPubMedGoogle Scholar
  64. 64.
    Hillengass J, Zechmann C, Bauerle T, Wagner-Gund B, Heiss C, Benner A, et al. Dynamic contrast-enhanced magnetic resonance imaging identifies a subgroup of patients with asymptomatic monoclonal plasma cell disease and pathologic microcirculation. Clin Cancer Res 2009;15:3118–25.CrossRefPubMedGoogle Scholar
  65. 65.
    Sahani DV, Holalkere NS, Mueller PR, Zhu AX. Advanced hepatocellular carcinoma: CT perfusion of liver and tumor tissue—initial experience. Radiology 2007;243:736–43.CrossRefPubMedGoogle Scholar
  66. 66.
    d’Assignies G, Couvelard A, Bahrami S, Vullierme MP, Hammel P, Hentic O, et al. Pancreatic endocrine tumors: tumor blood flow assessed with perfusion CT reflects angiogenesis and correlates with prognostic factors. Radiology 2009;250:407–16.CrossRefPubMedGoogle Scholar
  67. 67.
    Ash L, Teknos TN, Gandhi D, Patel S, Mukherji SK. Head and neck squamous cell carcinoma: CT perfusion can help noninvasively predict intratumoral microvessel density. Radiology 2009;251:422–8.CrossRefPubMedGoogle Scholar
  68. 68.
    Bisdas S, Baghi M, Smolarz A, Pihno NC, Lehnert T, Knecht R, et al. Quantitative measurements of perfusion and permeability of oropharyngeal and oral cavity cancer, recurrent disease, and associated lymph nodes using first-pass contrast-enhanced computed tomography studies. Invest Radiol 2007;42:172–9.CrossRefPubMedGoogle Scholar
  69. 69.
    Goh V, Halligan S, Daley F, Wellsted DM, Guenther T, Bartram CI. Colorectal tumor vascularity: quantitative assessment with multidetector CT—do tumor perfusion measurements reflect angiogenesis? Radiology 2008;249:510–7.CrossRefPubMedGoogle Scholar
  70. 70.
    Padhani AR. MRI for assessing antivascular cancer treatments. Br J Radiol 2003;76:60–80.CrossRefGoogle Scholar
  71. 71.
    Miller JC, Pien HH, Sahani D, Sorensen AG, Thrall JH. Imaging angiogenesis: applications and potential for drug development. J Natl Cancer Inst 2005;97:172–87.CrossRefPubMedGoogle Scholar
  72. 72.
    Kiessling F, Jugold M, Woenne EC, Brix G. Non-invasive assessment of vessel morphology and function in tumors by magnetic resonance imaging. Eur Radiol 2007;17:2136–48.CrossRefPubMedGoogle Scholar
  73. 73.
    Kiessling F, Farhan N, Lichy M, Vosseler S, Heilmann M, Krix M, et al. Dynamic contrast-enhanced magnetic resonance imaging rapidly indicates vessel regression in human squamous cell carcinomas grown in nude mice caused by VEGF receptor 2 blockade with DC101. Neoplasia 2004;6:213–23.CrossRefPubMedGoogle Scholar
  74. 74.
    Brasch R, Turetschek K. MRI characterization of tumors and grading angiogenesis using macromolecular contrast media: status report. Eur J Radiol 2000;34:148–55.CrossRefPubMedGoogle Scholar
  75. 75.
    Turetschek K, Preda A, Novikov V, Brasch RC, Weinmann HJ, Wunderbaldinger P, et al. Tumor microvascular changes in antiangiogenic treatment: assessment by magnetic resonance contrast media of different molecular weights. J Magn Reson Imaging 2004;20:138–44.CrossRefPubMedGoogle Scholar
  76. 76.
    Morgan B, Thomas AL, Drevs J, Hennig J, Buchert M, Jivan A, et al. Dynamic contrast-enhanced magnetic resonance imaging as a biomarker for the pharmacological response of PTK787/ZK 222584, an inhibitor of the vascular endothelial growth factor receptor tyrosine kinases, in patients with advanced colorectal cancer and liver metastases: results from two phase I studies. J Clin Oncol 2003;21:3955–64.CrossRefPubMedGoogle Scholar
  77. 77.
    O’Connor JP, Jackson A, Parker GJ, Jayson GC. DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents. Br J Cancer 2007;96:189–95.CrossRefPubMedGoogle Scholar
  78. 78.
    Meijerink MR, van Cruijsen H, Hoekman K, Kater M, van Schaik C, van Waesberghe JH, et al. The use of perfusion CT for the evaluation of therapy combining AZD2171 with gefitinib in cancer patients. Eur Radiol 2007;17:1700–13.CrossRefPubMedGoogle Scholar
  79. 79.
    Loo CE, Teertstra HJ, Rodenhuis S, van de Vijver MJ, Hannemann J, Muller SH, et al. Dynamic contrast-enhanced MRI for prediction of breast cancer response to neoadjuvant chemotherapy: initial results. AJR Am J Roentgenol 2008;191:1331–8.CrossRefPubMedGoogle Scholar
  80. 80.
    Park MS, Klotz E, Kim MJ, Song SY, Park SW, Cha SW, et al. Perfusion CT: noninvasive surrogate marker for stratification of pancreatic cancer response to concurrent chemo- and radiation therapy. Radiology 2009;250:110–7.CrossRefPubMedGoogle Scholar
  81. 81.
    Kiessling F, Boese J, Corvinus C, Ederle JR, Zuna I, Schoenberg SO, et al. Perfusion CT in patients with advanced bronchial carcinomas: a novel chance for characterization and treatment monitoring? Eur Radiol 2004;14:1226–33.PubMedGoogle Scholar
  82. 82.
    Nagashima T, Sakakibara M, Nakamura R, Arai M, Kadowaki M, Kazama T, et al. Dynamic enhanced MRI predicts chemosensitivity in breast cancer patients. Eur J Radiol 2006;60:270–4.CrossRefPubMedGoogle Scholar
  83. 83.
    Neff T, Kiessling F, Brix G, Baudendistel K, Zechmann C, Giesel FL, et al. An optimized workflow for the integration of biological information into radiotherapy planning: experiences with T1w DCE-MRI. Phys Med Biol 2005;50:4209–23.CrossRefPubMedGoogle Scholar
  84. 84.
    Khoo VS, Joon DL. New developments in MRI for target volume delineation in radiotherapy. Br J Radiol 2006;79:S2–15.CrossRefPubMedGoogle Scholar
  85. 85.
    MacManus M, Nestle U, Rosenzweig KE, Carrio I, Messa C, Belohlavek O, et al. Use of PET and PET/CT for radiation therapy planning: IAEA expert report 2006–2007. Radiother Oncol 2009;91:85–94.CrossRefPubMedGoogle Scholar
  86. 86.
    Nestle U, Weber W, Hentschel M, Grosu AL. Biological imaging in radiation therapy: role of positron emission tomography. Phys Med Biol 2009;54:R1–25.CrossRefPubMedGoogle Scholar
  87. 87.
    Koukourakis MI. Tumour angiogenesis and response to radiotherapy. Anticancer Res 2001;21:4285–300.PubMedGoogle Scholar
  88. 88.
    Loncaster JA, Carrington BM, Sykes JR, Jones AP, Todd SM, Cooper R, et al. Prediction of radiotherapy outcome using dynamic contrast enhanced MRI of carcinoma of the cervix. Int J Radiat Oncol Biol Phys 2002;54:759–67.PubMedGoogle Scholar
  89. 89.
    Newbold K, Castellano I, Charles-Edwards E, Mears D, Sohaib A, Leach M, et al. An exploratory study into the role of dynamic contrast-enhanced magnetic resonance imaging or perfusion computed tomography for detection of intratumoral hypoxia in head-and-neck cancer. Int J Radiat Oncol Biol Phys 2009;74:29–37.PubMedGoogle Scholar
  90. 90.
    Hall EJ. Radiobiology for the radiologist. 5th ed. Philadelphia: Lippincott Williams & Wilkins; 2000.Google Scholar
  91. 91.
    Hawighorst H, Knopp MV, Debus J, Hoffmann U, Grandy M, Griebel J, et al. Pharmacokinetic MRI for assessment of malignant glioma response to stereotactic radiotherapy: initial results. J Magn Reson Imaging 1998;8:783–8.CrossRefPubMedGoogle Scholar
  92. 92.
    Fuss M, Wenz F, Essig M, Muenter M, Debus J, Herman TS, et al. Tumor angiogenesis of low-grade astrocytomas measured by dynamic susceptibility contrast enhanced MRI (DSC-MRI) is predictive of local tumor control following radiation therapy. Int J Radiat Oncol Biol Phys 2001;51:478–82.PubMedGoogle Scholar
  93. 93.
    Law M, Young RJ, Babb JS, Peccerelli N, Chheang S, Gruber ML, et al. Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 2008;247:490–8.CrossRefPubMedGoogle Scholar
  94. 94.
    Zahra MA, Tan LT, Priest AN, Graves MJ, Arends M, Crawford RA, et al. Semiquantitative and quantitative dynamic contrast-enhanced magnetic resonance imaging measurements predict radiation response in cervix cancer. Int J Radiat Oncol Biol Phys 2009;74:766–73.PubMedGoogle Scholar
  95. 95.
    Cao Y, Popovtzer A, Li D, Chepeha DB, Moyer JS, Prince ME, et al. Early prediction of outcome in advanced head-and-neck cancer based on tumor blood volume alterations during therapy: a prospective study. Int J Radiat Oncol Biol Phys 2008;72:1287–90.PubMedGoogle Scholar
  96. 96.
    Devries AF, Griebel J, Kremser C, Judmaier W, Gneiting T, Kreczy A, et al. Tumor microcirculation evaluated by dynamic magnetic resonance imaging predicts therapy outcome for primary rectal carcinoma. Cancer Res 2001;61:2513–6.PubMedGoogle Scholar
  97. 97.
    Hawighorst H, Engenhart R, Knopp MV, Brix G, Grandy M, Essig M, et al. Intracranial meningeomas: time- and dose-dependent effects of irradiation on tumor microcirculation monitored by dynamic MR imaging. Magn Reson Imaging 1997;15:423–32.CrossRefPubMedGoogle Scholar
  98. 98.
    de Lussanet QG, Backes WH, Griffioen AW, Padhani AR, Baeten CI, van Baardwijk A, et al. Dynamic contrast-enhanced magnetic resonance imaging of radiation therapy-induced microcirculation changes in rectal cancer. Int J Radiat Oncol Biol Phys 2005;63:1309–15.CrossRefPubMedGoogle Scholar
  99. 99.
    Sahani DV, Kalva SP, Hamberg LM, Hahn PF, Willett CG, Saini S, et al. Assessing tumor perfusion and treatment response in rectal cancer with multisection CT: initial observations. Radiology 2005;234:785–92.CrossRefPubMedGoogle Scholar
  100. 100.
    Wenz F, Rempp K, Heß T, Debus J, Brix G, Engenhart R, et al. Effect of radiation on blood volume in low-grade astrocytomas and normal brain tissue: quantification using dynamic susceptibility contrast MR imaging. AJR Am J Roentgenol 1996;166:187–93.PubMedGoogle Scholar
  101. 101.
    Fuss M, Wenz F, Scholdei R, Essig M, Debus J, Knopp MV, et al. Radiation-induced regional cerebral blood volume (rCBV) changes in normal brain and low-grade astrocytomas: quantification and time and dose-dependent occurrence. Int J Radiat Oncol Biol Phys 2000;48:53–8.CrossRefPubMedGoogle Scholar
  102. 102.
    Pucar DM, Hricak H, Shukla-Dave A, Kuroiwa K, Drobnjak M, Eastharn K, et al. Clinically significant prostate cancer local recurrence after radiation therapy occurs at the site of primary tumor: magnetic resonance imaging and step-section pathology evidence. Int J Radiat Oncol Biol Phys 2007;69:62–8.PubMedGoogle Scholar
  103. 103.
    Kanwal RP. Generalized functions: theory and applications. 3rd ed. Boston: Birkhäuser; 2004.Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Gunnar Brix
    • 1
    • 4
  • Jürgen Griebel
    • 1
  • Fabian Kiessling
    • 2
  • Frederik Wenz
    • 3
  1. 1.Department of Medical and Occupational Radiation ProtectionFederal Office for Radiation ProtectionOberschleissheimGermany
  2. 2.Department of Experimental Molecular ImagingRWTH-Aachen UniversityAachenGermany
  3. 3.Department of Radiation OncologyUniversity Medical Center Mannheim, University of HeidelbergMannheimGermany
  4. 4.Abteilung für medizinischen und beruflichen StrahlenschutzBundesamt für Strahlenschutz (BfS)OberschleissheimGermany

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