Skip to main content
SpringerLink
Log in

Using Single-Channel Blind Deconvolution to Choose the Most Realistic Pharmacokinetic Model in Dynamic Contrast-Enhanced MR Imaging

  • Published:
Applied Magnetic Resonance Aims and scope Submit manuscript

Abstract

In dynamic contrast-enhanced magnetic resonance imaging, there has been no consensus in the choice of the pharmacokinetic model. In this paper, a new approach for assessment of the most realistic model for a given tissue is presented. Non-blind and single-channel blind deconvolution algorithms were used in quantitative magnetic resonance dynamic contrast-enhanced imaging of the mouse masseter muscle to compare the realism of two different pharmacokinetic models for the tissue residue function. The first was the adiabatic approximation tissue homogeneity model (aaJW) and the second, the two-compartment exchange model (2CXM). Normals and mice treated with the substance C48/80 were studied. C48/80 increases both blood flow and contrast leakage in muscle substantially. The obtained approximation accuracy was evaluated for both pharmacokinetic models. In addition, the arterial input functions (aifs) estimated using blind deconvolution were compared to the corresponding observed aifs. The hypothesis is that the most realistic model of the tissue residue function leads to the best fits. The non-blind deconvolution did not result in any clear answer. For blind deconvolution, the aifs of the aaJW model were very similar to the corresponding observed aifs, and clearly more so than the aifs of the 2CXM model. Also, the approximation of the observed tracer time sequences was more accurate for the aaJW than the 2CXM model. The realism of different pharmacokinetic models in describing the passage of a tracer through a microvascular bed of a single tissue could be assessed using single-channel blind deconvolution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. L. Hermoye, L. Annet, P. Lemmerling, F. Peeters, F. Jamar, P. Gianello, S. Van Huffel, B.E. Van Beers, Magn. Reson. Med. 51, 1017 (2004)

    Article  Google Scholar 

  2. R. Materne, A.M. Smith, F. Peeters, J.P. Dehoux, A. Keyeux, Y. Horsmans, B.E. Van Beers, Magn. Reson. Med. 47, 135 (2002)

    Article  Google Scholar 

  3. O. Keunen, M. Johansson, A. Oudin, M. Sanzey, S.A.A. Rahim, F. Fack, F. Thorsen, T. Taxt, M. Bartos, R. Jirik, H. Miletic, J.A. Wang, D. Stieber, L. Stuhr, I. Moen, C.B. Rygh, R. Bjerkvig, S.P. Niclou, PNAS 108, 3749 (2011)

    Article  ADS  Google Scholar 

  4. G. Brix, F. Kiessling, R. Lucht, S. Darai, K. Wasser, S. Delorme, J. Griebel, Magn. Reson. Med. 52, 420 (2004)

    Article  Google Scholar 

  5. P. Choyke, A. Dwyer, M. Knopp, J. Magn. Reson. Imag. 17, 509 (2003)

    Article  Google Scholar 

  6. I.S. Gribbestad, K.I. Gjesdal, G. Nilsen, S. Lundgren, M.H.B. Hjelstuen, A. Jackson, in Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Oncology, ed. by A. Jackson, D. Buckley, G. Parker, Medical Radiology (Berlin: Springer, 2005), pp. 3–22

  7. C. Kuhl, H. Schild, J. Magn. Reson. Imag. 12, 965 (2000)

    Article  Google Scholar 

  8. J. Barkhausen, P. Hunold, M. Jochims, J. Debatin, J. Magn. Reson. Imag. 19(6), 750 (2004)

    Article  Google Scholar 

  9. F.H. Epstein, J.F. London, D.C. Peters, L.M. Goncalves, K. Agyeman, J. Taylor, R.S. Balaban, A.E. Arai, Magn. Reson. Med. 47, 482 (2002)

    Article  Google Scholar 

  10. P. Germain, G. Roul, J. Baruthio, C. Jahn, P.M. Coulbois, B. Dumitresco, J.L. Dietemann, P. Bareiss, A. Constantinesco, Myocardial flow reserve parametric map, assessed by first-pass MRI compartmental analysis at the chronic stage of infarction. J. Magn. Reson. Imag. 13(3), 352–360 (2001)

    Article  Google Scholar 

  11. T. Koh, S. Bisdas, D. Koh, C. Thng, J. Magn. Reson. Imag. 34, 1262 (2011)

    Article  Google Scholar 

  12. S. Sourbron, D. Buckley, Phys. Med. Biol. 57, 1 (2012)

    Article  Google Scholar 

  13. P.S. Tofts, G. Brix, D.L. Buckley, J.L. Evelhoch, E. Henderson, M. Knopp, H.B.W. Larsson, T.Y. Lee, N.A. Mayr, G.J.M. Parker, R.E. Port, J. Taylor, R.M. Weisskoff, J. Magn. Reson. Imag. 10, 223 (1999)

    Article  Google Scholar 

  14. E. Henderson, J. Sykes, D. Drost, H.J. Weinmann, B.K. Rutt, T.Y. Lee, J. Magn. Reson. Imag. 12, 991 (2000)

    Article  Google Scholar 

  15. K. St. Lawrence, T. Lee, J. Cereb, Blood Flow Metab. 18, 1365 (1998)

  16. D. Buckley, Magn. Reson. Med. 47, 601 (2002)

    Article  Google Scholar 

  17. P. Campisi, K. Egiazarian, Blind Image Deconvolution. Theory and Applications (CRC Press, New York, 2007)

    Book  MATH  Google Scholar 

  18. D. Fish, A. Brinicombe, P. ER. J. Opt. Soc. Am. A 12, 58 (1995)

    Article  ADS  Google Scholar 

  19. J. Fluckinger, M. Schabel, E. DiBella, Magn. Reson. Med. 62, 1477 (2009)

    Article  Google Scholar 

  20. R. Lagendijk, J. Biemond, Iterative Identification and Restoration of Images (Kluwer Academic Publishers, Boston, 1991)

    Book  MATH  Google Scholar 

  21. J. Starck, E. Pantin, F. Murtagh, Publicat. Astron. Soc. Pac. 114, 1051 (2002)

    Article  ADS  Google Scholar 

  22. C. Yang, G. Karczmar, M. Medved, W. Stadler, Magn. Reson. Med. 58, 1266 (2007)

    Article  Google Scholar 

  23. T. Taxt, R. Jirik, C.B. Rygh, R. Gruner, M. Bartos, E. Andersen, F.R. Curry, R.K. Reed, IEEE Trans. Biomed. Eng. 59, 1012 (2012)

    Article  Google Scholar 

  24. J. Boesiger, M. Tsai, M. Maurer, M. Yamaguchi, L.F. Brown, K.P. Claffey, H.F. Dvorak, S.J. Galli, J. Exp. Med. 188, 1135 (1998)

    Article  Google Scholar 

  25. M. Koller, K. Woie, R. Reed, J. Appl. Physiol. 74, 2135 (1993)

    Google Scholar 

  26. A. Rosen, P. Strandberg, B. Unvaes, Acta Pharmacol. Toxicol. 13, 267 (1957)

    Article  Google Scholar 

  27. A. Rothschild, Br. J. Pharmacol. 38, 253 (1970)

    Article  Google Scholar 

  28. M. Cheng, G. Wright, Magn. Reson. Med. 55, 566 (2006)

    Article  Google Scholar 

  29. S.B. Donaldson, C.M.L. West, S.E. Davidson, B.M. Carrington, G. Hutchison, A.P. Jones, S.P. Sourbron, D.L. Buckley, Magn. Reson. Med. 63, 691 (2010)

    Article  Google Scholar 

  30. S. Sourbron, M. Ingrisch, A. Siefert, M. Reiser, K. Herrmann, Quantification of cerebral blood flow, cerebral blood volume, and blood–brain-barrier leakage with DCE-MRI. Magn. Reson. Med. 62(1), 205–217 (2009)

    Article  Google Scholar 

  31. R. Reed, H. Wiig, Acta Physiol. Scand. 113, 297305 (1981)

    Google Scholar 

  32. M. Hollander, D. Wolfe, Nonparametric Statistical Methods (Wiley, New York, 1973)

    MATH  Google Scholar 

  33. D. Ragan, S. Lai, J. Bankson, NMR Biomed. 24, 373 (2011)

    Google Scholar 

  34. M. Heilmann, C. Walczak, J. Vautier, J.L. Dimicoli, C.D. Thomas, M. Lupu, J. Mispelter, A. Volk, Magn. Reson. Mater. Phys. 20, 193 (2007)

    Article  Google Scholar 

  35. D. Checkley, J.J.L. Tessier, S.R. Wedge, M. Dukes, J. Kendrew, B. Curry, B. Middleton, J.C. Waterton, Magn. Reson. Med. 21, 475 (2003)

    Google Scholar 

  36. T. Koh, D. Cheong, Z. Hou, Magn. Reson. Med. 66, 886 (2011)

    Article  Google Scholar 

  37. Z. Rumboldt, R. Al-Okaili, J.P. Deveikis, AJNR Am. J. Neuroradiol. 26(5), 1178 (2005)

    Google Scholar 

  38. D.B. Matic, T.Y. Lee, R.G. Wells, B.S. Gan, Plast. Reconstr. Surg. 120(7), 1823 (2007). doi:10.1097/01.prs.0000287135.17291.2f

    Article  Google Scholar 

  39. V. Goh, S. Halligan, J.A. Hugill, C.I. Bartram, Am. J. Roentgenol. 187(1), 164 (2006)

    Article  Google Scholar 

  40. J. Bangsbo, P. Krustrup, J. González-Alonso, R. Boushel, B. Saltin, Am. J. Physiol. Regul. Integr. Comp. Physiol. 279(3), R899 (2000)

  41. C.R. Honig, M.L. Feldstein, J.L. Frierson, Am. J. Physiol. Heart Circ. Physiol. 233(1), H122 (1977)

    Google Scholar 

  42. M. Cao, Y. Liang, C. Shen, K. Miller, K. Stantz, IEEE Trans. Med. Imaging 28(6), 861 (2009)

    Article  Google Scholar 

  43. S.M. Galbraith, M.A. Lodge, N.J. Taylor, G.J.S. Rustin, S. Bentzen, J.J. Stirling, A.R. Padhani, NMR Biomed. 15(2), 132 (2002)

    Article  Google Scholar 

  44. M.M. Pike, C.N. Stoops, C.P. Langford, N.S. Akella, L.B. Nabors, G.Y. Gillespie, Magn. Reson. Med. 61, 615 (2009)

    Article  Google Scholar 

  45. S. Selinger, R. Bland, R. Demling, N. Staub, J. Appl. Physiol. 39, 773 (1975)

    Google Scholar 

  46. L. Smaje, B. Zweifach, M. Intaglietta, Microvasc. Res. 2, 96 (1970)

    Article  Google Scholar 

  47. D. Balvay, Y. Ponvianne, M. Claudon, C. Cuneod, in Proceedings of the 5th IEEE International Symposium on Biomedical Imaging, Paris, 2008, pp. 600–603

  48. D.M. McGrath, D.P. Bradley, J.L. Tessier, T. Lacey, C.J. Taylor, G.J.M. Parker, Magn. Reson. Med. 61, 1173 (2009)

    Article  Google Scholar 

  49. J. Johnson, T.A. Wilson, J. Physiol. 210, 1299 (1966)

    Google Scholar 

  50. T.S. Koh, V. Zeman, J. Darko, T.Y. Lee, M.F. Milosevic, M. Haider, P. Warde, I.W.T. Yeung, Phys. Med. Biol. 46, 1519 (2001)

    Article  Google Scholar 

Download references

Acknowledgments

We are grateful to Gerd Signe Salvesen for skilled technical support. This study was supported by Grants 911392, 911383 and PK 1760 (MedViz) of Helse Vest, Grant GA102/12/2380 of the Czech Science Foundation and by the Grant no. LO1212 of Ministry of Education, Youth, and Sports of the Czech Rep.

Author information

Authors and Affiliations

  1. Department of Biomedicine, University of Bergen, Jonas Lies vei 91, 5020, Bergen, Norway

    Torfinn Taxt, Tina Pavlin & Rolf K. Reed

  2. Department of Radiology, Haukeland University Hospital, Jonas Lies vei 83, 5020, Bergen, Norway

    Torfinn Taxt & Tina Pavlin

  3. Centre for Cancer Biomarkers (CCBIO), University of Bergen, Jonas Lies vei 87, 5021, Bergen, Norway

    Rolf K. Reed

  4. Department of Physiology and Membrane Biology, University of California, 4303 Tupper Hall One Shields Avenue, Davis, CA, 95616, USA

    Fitz-Roy Curry

  5. Department of Clinical Engineering, Haukeland University Hospital, Jonas Lies vei 83, 5020, Bergen, Norway

    Erling Andersen

  6. Academy of Sciences of the Czech Republic, Institute of Scientific Instruments, Královopolská 147, 61264, Brno, Czech Republic

    Radovan Jiřík

Authors
  1. Torfinn Taxt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tina Pavlin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rolf K. Reed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fitz-Roy Curry
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Erling Andersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Radovan Jiřík
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Radovan Jiřík.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Taxt, T., Pavlin, T., Reed, R.K. et al. Using Single-Channel Blind Deconvolution to Choose the Most Realistic Pharmacokinetic Model in Dynamic Contrast-Enhanced MR Imaging. Appl Magn Reson 46, 643–659 (2015). https://doi.org/10.1007/s00723-015-0679-y

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00723-015-0679-y

Keywords

Search

Navigation