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Maximum Likelihood Correction of Shape Bias Arising from Imaging Protocol: Application to Cardiac MRI

  • Pau Medrano-Gracia
  • David A. Bluemke
  • Brett R. Cowan
  • J. Paul Finn
  • Carissa G. Fonseca
  • João A. C. Lima
  • Avan Suinesiaputra
  • Alistair A. Young
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7085)

Abstract

To establish a fair comparison between shape models derived from different imaging protocols, a mapping correcting local bias must be applied. In this paper, a multi-dimensional statistical model has been investigated to correct the systematic differences between Steady-State Free Precession (SSFP) and Gradient Recalled Echo (GRE) cardiac MRI protocols. This statistical model makes use of the Maximum Likelihood (ML) approach to estimate the local parameters of the respective GRE and SSFP distributions. Once those parameters are known, a local mapping can be applied. We applied this method to 46 normal volunteers who were imaged with both protocols. The SSFP model was estimated from the corresponding GRE model and validation was performed with leave-one-out experiments. The error was examined in both the local model parameters and the clinically important global mass and volume estimates. Results showed that the systematic bias around the apex and papillary muscles could be locally corrected and that the mapping also provided a global correction in cavity volume (average error of 0.4 ±12.4 ml) and myocardial mass (− 1.2 ±11.1 g).

Keywords

Statistical Model Cardiac Magnetic Resonance Imaging (MRI) Finite Element Modelling Steady-State Free Precession (SSFP) Gradient Recalled Echo (GRE) Protocol Correction 

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References

  1. 1.
    Malayeri, A.A., Johnson, W.C., Macedo, R., Bathon, J., Lima, J.A.C., Bluemke, D.A.: Cardiac cine MRI: Quantification of the relationship between fast gradient echo and steady-state free precession for determination of myocardial mass and volumes. Journal of Magnetic Resonance Imaging 28(1), 60 (2008)CrossRefGoogle Scholar
  2. 2.
    Fonseca, C.G., Backhaus, M., Bluemke, D.A., Britten, R., Chung, J.D., Cowan, B.R., Dinov, I., Finn, J.P., Hunter, P.J., Kadish, A.H., Lee, D.C., Lima, J.A.C., Medrano-Gracia, P., Shivkumar, K., Tao, W., Young, A.A.: The Cardiac Atlas Project – An Imaging Database for Computational Modeling and Statistical Atlases of the Heart. Bioinformatics (in press, 2011)Google Scholar
  3. 3.
    Bild, D., Bluemke, D., Burke, G., Detrano, R., Diez Roux, A., Folsom, A., Greenland, P., et al.: Multi-Ethnic Study of Atherosclerosis: objectives and design. American Journal of Epidemiology 156(9), 871 (2002)CrossRefGoogle Scholar
  4. 4.
    Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging 17(1), 87–97 (1998)CrossRefGoogle Scholar
  5. 5.
    Hofmann, M., Steinke, F., Scheel, V., Charpiat, G., Farquhar, J., Aschoff, P., Brady, M., Scholkopf, B., Pichler, B.J.: MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration. Journal of Nuclear Medicine 49(11), 1875 (2008)CrossRefGoogle Scholar
  6. 6.
    Beyer, T., Weigert, M., Quick, H., Pietrzyk, U., Vogt, F., Palm, C., Antoch, G., Müller, S., Bockisch, A.: MR-based attenuation correction for torso-PET/MR imaging: pitfalls in mapping MR to CT data. European Journal of Nuclear Medicine and Molecular Imaging 35(6), 1142–1146 (2008)CrossRefGoogle Scholar
  7. 7.
    Medrano-Gracia, P., Backhaus, M., Bluemke, D.A., Chung, J.D., Cowan, B.R., Finn, J.P., Fonseca, C.G., Hunter, P.J., Kadish, A.H., Lee, D.C., Lima, J.A.C., Shivkumar, K., Tao, W., Young, A.A.: The Cardiac Atlas Project: Rationale, Design and Preliminary Results. Journal of Cardiovascular Magnetic Resonance 13(suppl. 1), O72 (2011)CrossRefGoogle Scholar
  8. 8.
    Young, A.A., Cowan, B.R., Thrupp, S.F., Hedley, W.J., Dell’Italia, L.J.: Left Ventricular Mass and Volume: Fast Calculation with Guide-Point Modeling on MR Images. Radiology 216(2), 597 (2000)CrossRefGoogle Scholar
  9. 9.
    Nielsen, P.M., Le Grice, I., Smaill, B.H., Hunter, P.J.: Mathematical model of geometry and fibrous structure of the heart. American Journal of Physiology- Heart and Circulatory Physiology 260(4), H1365 (1991)Google Scholar
  10. 10.
    Stephens, M.A.: EDF statistics for goodness of fit and some comparisons. Journal of the American Statistical Association 69(347), 730–737 (1974)CrossRefGoogle Scholar
  11. 11.
    Bland, J.M., Altman, D.G.: Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1(8476), 307–310 (1986)CrossRefGoogle Scholar
  12. 12.
    Cowan, B., Young, A., Anderson, C., Doughty, R., Krittayaphong, R., Lonn, E., Marwick, T., Reid, C., Sanderson, J., Schmieder, R., et al.: Left ventricular mass and volume with telmisartan, ramipril, or combination in patients with previous atherosclerotic events or with diabetes mellitus (from the ONgoing Telmisartan Alone and in Combination With Ramipril Global Endpoint Trial [ONTARGET]). The American Journal of Cardiology 104(11), 1484–1489 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pau Medrano-Gracia
    • 1
  • David A. Bluemke
    • 4
  • Brett R. Cowan
    • 1
  • J. Paul Finn
    • 2
  • Carissa G. Fonseca
    • 2
  • João A. C. Lima
    • 3
  • Avan Suinesiaputra
    • 1
  • Alistair A. Young
    • 1
  1. 1.Auckland Bioengineering InstituteUniversity of AucklandNew Zealand
  2. 2.Diagnostic CardioVascular ImagingUniversity of CaliforniaLos AngelesUSA
  3. 3.The Donald W. Reynolds Cardiovascular Clinical Research CenterThe Johns Hopkins UniversityUSA
  4. 4.NIH Clinical CenterUSA

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