Table of Contents
ISRN Neuroscience
Volume 2013, Article ID 905279, 6 pages
http://dx.doi.org/10.1155/2013/905279
Clinical Study

Measurement of Blood-Brain Barrier Permeability with T1-Weighted Dynamic Contrast-Enhanced MRI in Brain Tumors: A Comparative Study with Two Different Algorithms

1Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal and Child Health, University of Genoa, 16132 Genoa, Italy
2Magnetic Resonance Research Centre on Nervous System Diseases, University of Genoa, 16132 Genoa, Italy
3Department of Diagnostic and Interventional Neuroradiology, San Martino University Hospital, 16132 Genoa, Italy
4Department of Pathology, San Martino University Hospital, 16132 Genoa, Italy
5Department of Health Sciences, University of Genoa, 16132 Genoa, Italy

Received 24 December 2012; Accepted 16 January 2013

Academic Editors: C. Bishop and H. Ochi

Copyright © 2013 Maurizio Bergamino et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. I. Gaitán, C. D. Shea, I. E. Evangelou et al., “Evolution of the blood-brain barrier in newly forming multiple sclerosis lesions,” Annals of Neurology, vol. 70, no. 1, pp. 22–29, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Taheri, C. Gasparovic, B. N. Huisa et al., “Blood-brain barrier permeability abnormalities in vascular cognitive impairment,” Stroke, vol. 42, no. 8, pp. 2158–2163, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Kassner, D. M. Mandell, and D. J. Mikulis, “Measuring permeability in acute ischemic stroke,” Neuroimaging Clinics of North America, vol. 21, no. 2, pp. 315–325, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. A. P. Viggars, S. B. Wharton, J. E. Simpson et al., “Alterations in the blood brain barrier in ageing cerebral cortex in relationship to Alzheimer-type pathology: a study in the MRC-CFAS population neuropathology cohort,” Neuroscience Letters, vol. 505, no. 1, pp. 25–30, 2011. View at Publisher · View at Google Scholar
  5. H. B. W. Larsson and P. S. Tofts, “Measurement of blood-brain barrier permeability using dynamic Gd-DTPA scanning—a comparison of methods,” Magnetic Resonance in Medicine, vol. 24, no. 1, pp. 174–176, 1992. View at Google Scholar · View at Scopus
  6. S. Cha, L. Yang, G. Johnson et al., “Comparison of microvascular permeability measurements, Ktrans, determined with conventional steady-state T1-weighted and first-pass T2-weighted MR imaging methods in gliomas and meningiomas,” American Journal of Neuroradiology, vol. 27, no. 2, pp. 409–417, 2006. View at Google Scholar · View at Scopus
  7. H. C. Roberts, T. P. L. Roberts, R. C. Brasch, and W. P. Dillon, “Quantitative measurement of microvascular permeability in human brain tumors achieved using dynamic contrast-enhanced MR imaging: correlation with histologic grade,” American Journal of Neuroradiology, vol. 21, no. 5, pp. 891–899, 2000. View at Google Scholar · View at Scopus
  8. H. C. Schwickert, M. Stiskal, T. P. L. Roberts et al., “Contrast-enhanced MR imaging assessment of tumor capillary permeability: effect of irradiation on delivery of chemotherapy,” Radiology, vol. 198, no. 3, pp. 893–898, 1996. View at Google Scholar · View at Scopus
  9. H. Daldrup, D. M. Shames, M. Wendland et al., “Correlation of dynamic contrast-enhanced MR imaging with histologic tumor grade: comparison of macromolecular and small-molecular contrast media,” American Journal of Roentgenology, vol. 171, no. 4, pp. 941–949, 1998. View at Google Scholar · View at Scopus
  10. P. S. Tofts, G. Brix, D. L. Buckley et al., “Estimating kinetic parameters from dynamic contrast-enhanced T1-weighted MRI of a diffusable tracer: standardized quantities and symbols,” Journal of Magnetic Resonance Imaging, vol. 10, no. 3, pp. 223–232, 1999. View at Google Scholar
  11. D. L. Buckley, “Uncertainty in the analysis of tracer kinetics using dynamic contrast-enhanced T1weighted MRI,” Magnetic Resonance in Medicine, vol. 47, no. 3, pp. 601–606, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. P. S. Tofts, “Modeling tracer kinetics in dynamic Gd-DTPA MR imaging,” Journal of Magnetic Resonance Imaging, vol. 7, no. 1, pp. 91–101, 1997. View at Publisher · View at Google Scholar · View at Scopus
  13. L. E. Kershaw and H. L. M. Cheng, “Temporal resolution and SNR requirements for accurate DCE-MRI data analysis using the AATH model,” Magnetic Resonance in Medicine, vol. 64, no. 6, pp. 1772–1780, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. T. S. Koh, D. L. Cheong, and Z. Hou, “Issues of discontinuity in the impulse residue function for deconvolution analysis of dynamic contrast-enhanced MRI data,” Magnetic Resonance in Medicine, vol. 66, no. 3, pp. 886–892, 2011. View at Publisher · View at Google Scholar
  15. G. Brix, M. S. Ravesh, S. Zwick, J. Griebel, and S. Delorme, “On impulse response functions computed from dynamic contrast-enhanced image data by algebraic deconvolution and compartmental modeling,” Physica Medica, vol. 28, no. 2, pp. 119–128, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. U. Hoffmann, G. Brix, M. V. Knopp, T. Hess, and W. J. Lorenz, “Pharmacokinetic mapping of the breast: a new method for dynamic MR mammography,” Magnetic Resonance in Medicine, vol. 33, no. 4, pp. 506–514, 1995. View at Publisher · View at Google Scholar · View at Scopus
  17. H. B. W. Larsson, F. Courivaud, E. Rostrup, and A. E. Hansen, “Measurement of brain perfusion, blood volume, and blood-brain barrier permeability, using dynamic contrast-enhanced T1-weighted MRI at 3 tesla,” Magnetic Resonance in Medicine, vol. 62, no. 5, pp. 1270–1281, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. T. S. Koh, V. Zeman, J. Darko et al., “The inclusion of capillary distribution in the adiabatic tissue homogeneity model of blood flow,” Physics in Medicine and Biology, vol. 46, no. 5, pp. 1519–1538, 2001. View at Publisher · View at Google Scholar · View at Scopus
  19. M. C. Schabel, “A unified impulse response model for DCE-MRI,” Magnetic Resonance in Medicine, vol. 68, no. 5, pp. 1632–1646, 2012. View at Publisher · View at Google Scholar
  20. C. S. Patlak, R. G. Blasberg, and J. D. Fenstermacher, “Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data,” Journal of Cerebral Blood Flow and Metabolism, vol. 3, no. 1, pp. 1–7, 1983. View at Google Scholar · View at Scopus
  21. D. N. Louis, H. Ohgaki, O. D. Wiestler et al., “The 2007 WHO classification of tumours of the central nervous system,” Acta Neuropathologica, vol. 114, no. 2, pp. 97–109, 2007. View at Publisher · View at Google Scholar
  22. W. T. I. Yeung, T. Y. Lee, R. F. Del Maestro, R. Kozak, and T. Brown, “In vivo CT measurement of blood-brain transfer constant of iopamidol in human brain tumors,” Journal of Neuro-Oncology, vol. 14, no. 2, pp. 177–187, 1992. View at Google Scholar · View at Scopus
  23. R. A. Hawkins, M. E. Phelps, and S. C. Huang, “A kinetic evaluation of blood-brain barrier permeability in human brain tumors with [68Ga]EDTA and positron computed tomography,” Journal of Cerebral Blood Flow and Metabolism, vol. 4, no. 4, pp. 507–515, 1984. View at Google Scholar · View at Scopus
  24. C. Roberts, B. Issa, A. Stone, A. Jackson, J. C. Waterton, and G. J. M. Parker, “Comparative study into the robustness of compartmental modeling and model-free analysis in DCE-MRI studies,” Journal of Magnetic Resonance Imaging, vol. 23, no. 4, pp. 554–563, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. H. Bagher-Ebadian, R. Jain, S. P. Nejad-Davarani et al., “Model selection for DCE-T1 studies in glioblastoma,” Magnetic Resonance in Medicine, vol. 68, pp. 241–251, 2011. View at Google Scholar
  26. D. P. Barboriak, J. R. MacFall, A. O. Padua, G. E. York, B. L. Viglianti, and M. W. Dewhirst, “Standardized software for calculation of Ktrans and vp from dynamic T1-weighted MR images,” in Proceedings of the International Society for Magnetic Resonance in Medicine Workshop on MR in Drug Development: From Discovery to Clinical Therapeutic Trials, McLean, Va, USA, 2004.
  27. M. Ingrisch, O. Dietrich, U. I. Attenberger et al., “Quantitative pulmonary perfusion magnetic resonance imaging: influence of temporal resolution and signal-to-noise ratio,” Investigative Radiology, vol. 45, no. 1, pp. 7–14, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. C. Lavini, J. J. Verhoeff, C. B. Majoie, L. J. Stalpers, D. J. Richel, and M. Maas, “Model-based, semiquantitative and time intensity curve shape analysis of dynamic contrast-enhanced MRI: a comparison in patients undergoing antiangiogenic treatment for recurrent glioma,” Journal of Magnetic Resonance Imaging, vol. 34, no. 6, pp. 1303–1312, 2011. View at Google Scholar
  29. A. Haase, “Snapshot FLASH MRI. Applications to T1, T2, and chemical-shift imaging,” Magnetic Resonance in Medicine, vol. 13, no. 1, pp. 77–89, 1990. View at Google Scholar · View at Scopus
  30. P. Caravan, J. J. Ellison, T. J. McMurry, and R. B. Lauffer, “Gadolinium(III) chelates as MRI contrast agents: structure, dynamics, and applications,” Chemical Reviews, vol. 99, no. 9, pp. 2293–2352, 1999. View at Publisher · View at Google Scholar · View at Scopus
  31. T. T. Batchelor, A. G. Sorensen, E. di Tomaso et al., “AZD2171, a Pan-VEGF receptor tyrosine kinase inhibitor, normalizes tumor vasculature and alleviates edema in glioblastoma patients,” Cancer Cell, vol. 11, no. 1, pp. 83–95, 2007. View at Publisher · View at Google Scholar · View at Scopus
  32. R. Jain, J. Gutierrez, J. Narang et al., “In vivo correlation of tumor blood volume and permeability with histologic and molecular angiogenic markers in gliomas,” American Journal of Neuroradiology, vol. 32, no. 2, pp. 388–394, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. J. U. Harrer, G. J. M. Parker, H. A. Haroon et al., “Comparative study of methods for determining vascular permeability and blood volume in human gliomas,” Journal of Magnetic Resonance Imaging, vol. 20, no. 5, pp. 748–757, 2004. View at Publisher · View at Google Scholar · View at Scopus
  34. R. E. Port, L. J. Bernstein, D. P. Barboriak, L. Xu, T. P. L. Roberts, and N. Van Bruggen, “Noncompartmental kinetic analysis of DCE-MRI data from malignant tumors: application to glioblastoma treated with bevacizumab,” Magnetic Resonance in Medicine, vol. 64, no. 2, pp. 408–417, 2010. View at Publisher · View at Google Scholar · View at Scopus
  35. M. Law, S. Yang, J. S. Babb et al., “Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade,” American Journal of Neuroradiology, vol. 25, no. 5, pp. 746–755, 2004. View at Google Scholar · View at Scopus
  36. T. F. Patankar, H. A. Haroon, S. J. Mills et al., “Is volume transfer coefficient (Ktrans) related to histologic grade in human gliomas?” American Journal of Neuroradiology, vol. 26, no. 10, pp. 2455–2465, 2005. View at Google Scholar · View at Scopus