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Modelling Parameter Role on Accuracy of Cardiac Perfusion Quantification

  • Niloufar Zarinabad
  • Amedeo Chiribiri
  • Gilion L. T. F. Hautvast
  • Andreas Shuster
  • Matthew Sinclair
  • Jeroen P. H. M. van den Wijngaard
  • Nicolas Smith
  • Jos A. E. Spaan
  • Maria Siebes
  • Marcel Breeuwer
  • Eike Nagel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7945)

Abstract

Cardiovascular magnetic resonance (CMR) perfusion data are suitable for quantitative measurement of myocardial blood flow. The goal of perfusion-CMR post- processing is to recover tissue impulse-response from observed signal-intensity curves. While several deconvolution techniques are available for this purpose, all of them use models with varying parameters for the representation of the impulse-response. However this variation influences the accuracy of the deconvolution and introduces possible variations in the results. Using an appropriate order for quantification is essential to allow CMR-perfusion-quantification to develop into a useful clinical tool. The aim of this study was to evaluate the effect of parameter variation in Fermi modelling, autoregressive moving-average model (ARMA), B-spline-basis and exponential-basis deconvolution. Whilst Fermi is the least dependent method on the modelling parameter determination, the B-spline and ARMA were the most sensitive models to this variation. ARMA upon a correct choice of order showed to be the superior to other methods.

Keywords

Perfusion quantification Deconvolution Model order 

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References

  1. 1.
    Ichihara, T., Ishida, M., Kitagawa, K., Ichikawa, Y., Natsume, T., Yamaki, N., Maeda, H., Takeda, K., Sakuma, H.: Quantitative analysis of first-pass contrast-enhanced myocardial perfusion MRI using a Patlak plot method and blood saturation correction. Magn. Reson. Med. 62, 373–383 (2009)CrossRefGoogle Scholar
  2. 2.
    Ishida, M., Morton, G., Schuster, A., Nagel, E., Chiribiri, A.: Quantitative Assessment of Myocardial Perfusion MRI. Curr. Cardiovasc. Imaging. Rep. 3, 8 (2010)CrossRefGoogle Scholar
  3. 3.
    Zierler, K.: Indicator dilution methods for measuring blood flow, volume, and other properties of biological systems: a brief history and memoir. Annals of Biomedical Engineering 28, 836–848 (2000)CrossRefGoogle Scholar
  4. 4.
    Jerosch-Herold, M.: Quantification of myocardial perfusion by cardiovascular magnetic resonance. J. Cardiovasc. Magn. Reson. 12, 57 (2010)CrossRefGoogle Scholar
  5. 5.
    Engl, H.W., Hanke, M., Neubauer, A.: Regularization of inverse problems. Kluwer Academic Publishers, Dordrecht (1996)zbMATHCrossRefGoogle Scholar
  6. 6.
    Gill, P.E., Murray, W., Wright, M.H.: Practical optimization. Academic Press, London (1981)zbMATHGoogle Scholar
  7. 7.
    Keeling, S.L., Kogler, T., Stollberger, R.: Deconvolution for DCE-MRI using an exponential approximation basis. Medical Image Analysis 13, 80–90 (2009)CrossRefGoogle Scholar
  8. 8.
    Pack, N.A., DiBella, E.V., Rust, T.C., Kadrmas, D.J., McGann, C.J., Butterfield, R., Christian, P.E., Hoffman, J.M.: Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution method. J. Cardiovasc. Magn. Reson. 10, 52 (2008)CrossRefGoogle Scholar
  9. 9.
    Pack, N.A., DiBella, E.V.: Comparison of myocardial perfusion estimates from dynamic contrast-enhanced magnetic resonance imaging with four quantitative analysis methods. Magn. Reson. Med. 64, 125–137 (2010)CrossRefGoogle Scholar
  10. 10.
    Zarinabad, N., Chiribiri, A., Hautvast, G.L., Ishida, M., Schuster, A., Cvetkovic, Z., Batchelor, P.G., Nagel, E.: Voxel-wise quantification of myocardial perfusion by cardiac magnetic resonance. Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 68, 1994–2004 (2012)CrossRefGoogle Scholar
  11. 11.
    Jerosch-Herold, M., Swingen, C., Seethamraju, R.T.: Myocardial blood flow quantification with MRI by model-independent deconvolution. Med. Phys. 29, 886–897 (2002)CrossRefGoogle Scholar
  12. 12.
    Jerosch-Herold, M., Wilke, N., Stillman, A.E.: Magnetic resonance quantification of the myocardial perfusion reserve with a Fermi function model for constrained deconvolution. Med. Phys. 25, 73–84 (1998)CrossRefGoogle Scholar
  13. 13.
    Batchelor, P., Chiribiri, A., Nooralipour, N.Z., Cvetkovic, Z.: Arma Regularization of Cardiac Perfusion Modeling. In: International Conference on Acoustics, Speech and Signal Processing, ICASSP 2010, pp. 642–645 (2010)Google Scholar
  14. 14.
    Neyran, B., Janier, M.F., Casali, C., Revel, D., Canet Soulas, E.P.: Mapping myocardial perfusion with an intravascular MR contrast agent: robustness of deconvolution methods at various blood flows. Magn. Reson. Med. 48, 166–179 (2002)CrossRefGoogle Scholar
  15. 15.
    Wang, L., Jerosch-Herold, M., Jacobs Jr., D.R., Shahar, E., Folsom, A.R.: Coronary risk factors and myocardial perfusion in asymptomatic adults: the Multi-Ethnic Study of Atherosclerosis (MESA). J. Am. Coll. Cardiol. 47, 565–572 (2006)CrossRefGoogle Scholar
  16. 16.
    Hautvast, G., Chiribiri, A., Zarinabad, N., Schuster, A., Breeuwer, M., Nagel, E.: Myocardial blood flow quantification from MRI by deconvolution using an exponential approximation basis. IEEE Transactions on Bio-medical Engineering 59, 2060–2067 (2012)CrossRefGoogle Scholar
  17. 17.
    Wilke, N., Jerosch-Herold, M., Wang, Y., Huang, Y., Christensen, B.V., Stillman, A.E., Ugurbil, K., McDonald, K., Wilson, R.F.: Myocardial perfusion reserve: assessment with multisection, quantitative, first-pass MR imaging. Radiology 204, 373–384 (1997)Google Scholar
  18. 18.
    Zarinabad, N., Hautvast, G., Breeuwer, M., Nagel, E., Chiribiri, A.: Effect of tracer arrival time on the estimation of the myocardial perfusion in DCE-CMR. Journal of Cardiovascular Magnetic Resonance 14, 16 (2012)CrossRefGoogle Scholar
  19. 19.
    Shuster, A.: Validation of Quantitative Myocardial Perfusion Magnetic Resonance Imaging. Division of imaging sciences and biomedical engineering, ph.D. King College London, London (2012)Google Scholar
  20. 20.
    Ishida, M., Schuster, A., Morton, G., Chiribiri, A., Hussain, S., Paul, M., Merkle, N., Steen, H., Lossnitzer, D., Schnackenburg, B., Alfakih, K., Plein, S., Nagel, E.: Development of a universal dual-bolus injection scheme for the quantitative assessment of myocardial perfusion cardiovascular magnetic resonance. J. Cardiovasc. Magn. Reson. 13, 28 (2011)CrossRefGoogle Scholar
  21. 21.
    van Horssen, P., Siebes, M., Hoefer, I., Spaan, J.A., van den Wijngaard, J.P.: Improved detection of fluorescently labeled microspheres and vessel architecture with an imaging cryomicrotome. Med. Biol. Eng. Comput. 48, 735–744 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Niloufar Zarinabad
    • 1
  • Amedeo Chiribiri
    • 1
  • Gilion L. T. F. Hautvast
    • 2
  • Andreas Shuster
    • 1
  • Matthew Sinclair
    • 1
  • Jeroen P. H. M. van den Wijngaard
    • 3
  • Nicolas Smith
    • 1
  • Jos A. E. Spaan
    • 3
  • Maria Siebes
    • 3
  • Marcel Breeuwer
    • 4
  • Eike Nagel
    • 1
  1. 1.Division of Imaging Sciences and Biomedical EngineeringKings College London, St. Thomas Hospital LondonUK
  2. 2.Philips Group Innovation – Healthcare IncubatorsEindhovenThe Netherlands
  3. 3.Department of Biomedical Engineering & PhysicsAcademic Medical CentreAmsterdamThe Netherlands
  4. 4.Philips Healthcares, Imaging Systems – MRBestThe Netherlands

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