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Attenuation Resilient AIF Estimation Based on Hierarchical Bayesian Modelling for First Pass Myocardial Perfusion MRI

  • Volker J. Schmid
  • Peter D. Gatehouse
  • Guang-Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4791)

Abstract

Non-linear attenuation of the Arterial Input Function (AIF) is a major problem in first-pass MR perfusion imaging due to the high concentration of the contrast agent in the blood pool. This paper presents a technique to reconstruct the true AIF using signal intensities in the myocardium and the attenuated AIF based on a Hierarchical Bayesian Model (HBM). With the proposed method, both the AIF and the response function are modeled as smoothed functions by using Bayesian penalty splines (P-Splines). The derived AIF is then used to estimate the impulse response of the myocardium based on deconvolution analysis. The proposed technique is validated both with simulated data using the MMID4 model and ten in vivo data sets for estimating myocardial perfusion reserve rates. The results demonstrate the ability of the proposed technique in accurately reconstructing the desired AIF for myocardial perfusion quantification. The method does not involve any MRI pulse sequence modification, and thus is expected to have wider clinical impact.

Keywords

Myocardial Perfusion Cardiovascular Magnetic Resonance Arterial Input Function Hierarchical Bayesian Model Myocardial Perfusion Reserve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Panting, J., Gatehouse, P., Yang, G.-Z., Grothues, F., Firmin, D., Collins, P., Pennell, D.: Abnormal subendocardial perfusion in cardiac syndrome X detected by cardiovascular MRI. New Engl. J. of Med. 346, 1948–1953 (2002)CrossRefGoogle Scholar
  2. 2.
    Kim, D., Axel, L.: Multislice, dual-imaging sequence for increasing the dynamic range of the contrast-enhanced blood signal and CNR of myocardial enhancement at 3T. J. of Mag. Res. Imag. 23, 81–86 (2006)CrossRefGoogle Scholar
  3. 3.
    Gatehouse, P., Elkington, A., Ablitt, N., Yang, G., Pennell, D., Firmin, D.: Accurate assesment of the arterial input function during high-dose myocardial perfusion cardiovascular magnetic resonance. J. Mag. Res. Imag. 20, 39–45 (2004)CrossRefGoogle Scholar
  4. 4.
    Christian, T.F., Rettmann, D.W., Aletras, A.H., Liao, S.L., Taylor, J.L., Balaban, R.S., Arai, A.E.: Absolute myocardial perfusion in canines measured by using dual-bolus first-pass MR imaging. Radiology 232, 677–684 (2004)CrossRefGoogle Scholar
  5. 5.
    Bellamy, D.D., Pereira, R.S., McKenzie, C.A., Prato, F.S., Drost, D.J., Sykes, J., Wisenberg, G.: Gd-DTPA bolus tracking in the myocardium using T1 fast acquisition relaxation mapping (T1 FARM). Magn. Res. in Med. 46, 555–564 (2001)CrossRefGoogle Scholar
  6. 6.
    Orton, M.R., Walker-Samuel, S., Collins, D.J., Leach, M.O.: A joint bayesian method for robust estimation of PK and AIF parameters for DCE-MR imaging. In: Proceedings of the 14th Annual Meeting of ISMRM, Seattle, p. 3490 (2006)Google Scholar
  7. 7.
    Lang, S., Brezger, A.: Bayesian P-splines. J. of Comp. and Graph Stat. 13, 183–212 (2004)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Kroll, K., Wilke, N., Jerosch-Herold, M., Wang, Y., Zhang, Y., Bache, R.J., Gassingthwaighte, J.B.: Modeling regional myocardial flows from residue functions of an intravascular indicator. Am. J. of Heart. Circ. Phys. 271, 1643–1655 (1996)Google Scholar
  9. 9.
    Gilks, W.R., Richardson, S., Spiegelhalter, D.J.: Markov Chain Monte Carlo in Practice. Chapman & Hall, London (1996)zbMATHGoogle Scholar
  10. 10.
    Jerosch-Herold, M., Swingen, C., Seethamraju, R.: Myocardial blood flow quantification with MRI by model-independent deconvolution. Med. Phys. 29(5), 886–897 (2002)CrossRefGoogle Scholar
  11. 11.
    Schmid, V.J, Whitcher, B., Yang, G.Z.: Semi-parametric analysis of dynamic contrast-enhanced MRI using Bayesian P-splines. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 679–686. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Volker J. Schmid
    • 1
  • Peter D. Gatehouse
    • 2
  • Guang-Zhong Yang
    • 1
  1. 1.Institute for Biomedical Engineering, Imperial College, South Kensington, LondonUnited Kingdom
  2. 2.Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, LondonUnited Kingdom

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