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Slice-Based Combination of Rest and Dobutamine–Stress Cardiac MRI Using a Statistical Motion Model to Identify Myocardial Infarction: Validation against Contrast-Enhanced MRI

  • Avan Suinesiaputra
  • Alejandro F. Frangi
  • Theodorus A. M. Kaandorp
  • Hildo J. Lamb
  • Jeroen J. Bax
  • Johan H. C. Reiber
  • Boudewijn P. F. Lelieveldt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6666)

Abstract

This paper presents an automated method for regional wall motion abnormality detection (RWMA) from rest and stress cardiac MRI. The automated RWMA detection is based on a statistical shape model of myocardial contraction trained on slice-based myocardial contours from in ED and ES. A combination of rigid and non-rigid registrations is introduced to align a patient shape to the normokinetic myocardium model, where pure contractility information is kept. The automated RWMA method is applied to identify potentially infarcted myocardial segments from rest–stress MRI alone.

In this study, 41 cardiac MRI studies of healthy subjects were used to build the statistical normokinetic model, while 12 myocardial infarct patients were included for validation. The rest–stress data produced a better separation between scar and normal segments compared to the rest–only data. The sensitivity, specificity and accuracy were increased by 34%, 30%, and 32%, respectively. The area under the ROC curve for the rest–stress data was improved to 0.87 compared to 0.63 for the rest–only data.

Keywords

Stress Data Regional Wall Motion Analysis Contractile Reserve Statistical Shape Model Infarct Transmurality 
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.

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References

  1. 1.
    Bookstein, F.L.: Principal Warps: Thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11(6), 567–585 (1989)CrossRefzbMATHGoogle Scholar
  2. 2.
    Caiani, E.G., Toledo, E., et al.: Automated interpretation of regional left ventricular wall motion from cardiac magnetic resonance images. J. Cardiovasc Magn. Reson. 8(3), 427–433 (2006)CrossRefGoogle Scholar
  3. 3.
    Dryden, I.L., Mardia, K.V.: Statistical shape analysis. John Wiley & Sons, Inc., Chichester (1998)zbMATHGoogle Scholar
  4. 4.
    van der Geest, R.J., Buller, V.G., et al.: Comparison between manual and semiautomated analysis of left ventricular volume parameters from short-axis MR images. J. Comput. Assist. Tomogr. 21(5), 756–765 (1997)CrossRefGoogle Scholar
  5. 5.
    Herz, S.L., Ingrassia, C.M., et al.: Parameterization of left ventricular wall motion for detection of regional ischemia. Ann. Biomed. Eng. 33(7), 912–919 (2005)CrossRefGoogle Scholar
  6. 6.
    Heusch, G., Schulz, R., Rahimtoola, S.H.: Myocardial hibernation: a delicate balance. Am. J. Physiol. Heart Circ. Physiol. 288(3), H984–H999 (2005)CrossRefGoogle Scholar
  7. 7.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, Inc., Chichester (2001)CrossRefGoogle Scholar
  8. 8.
    Kaandorp, T.A.M., Bax, J.J., Schuijf, J.D., et al.: Head-to-head comparison between contrast-enhanced magnetic resonance imaging and dobutamine magnetic resonance imaging in men with ischemic cardiomyopathy. Am. J. Cardiol. 93(12), 1461–1464 (2004)CrossRefGoogle Scholar
  9. 9.
    Kachenoura, N., Redheuil, A., et al.: Evaluation of regional myocardial function using automated wall motion analysis of cine MR images: Contribution of parametric images, contraction times, and radial velocities. J. Magn. Reson. Imaging 26(4), 1127–1132 (2007)CrossRefGoogle Scholar
  10. 10.
    Kim, R.J., Wu, E., Rafael, A., et al.: The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction. N. Engl. J. Med. 343(20), 1445–1453 (2000)CrossRefGoogle Scholar
  11. 11.
    Mahrholdt, H., Klem, I., Sechtem, U.: Cardiovascular MRI for detection of myocardial viability and ischaemia. Heart 93(1), 122–129 (2007)CrossRefGoogle Scholar
  12. 12.
    Qazi, M., Fung, G., et al.: Automated heart abnormality detection using sparse linear classifiers. IEEE Eng. Med. Biol. Mag. 26(2), 56–63 (2007)CrossRefGoogle Scholar
  13. 13.
    Saraste, A., Nekolla, S., Schwaiger, M.: Contrast-enhanced magnetic resonance imaging in the assessment of myocardial infarction and viability. J. Nucl. Cardiol. 15(1), 105–117 (2008)CrossRefGoogle Scholar
  14. 14.
    Sheather, S.J., Jones, M.C.: A reliable data-based bandwidth selection method for kernel density estimation. J. R. Stat. Soc. Series B Stat. Methodol. 53(3), 683–690 (1991)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Suinesiaputra, A., Frangi, A.F., Kaandorp, T.A.M., Lamb, H.J., Bax, J.J., Reiber, J.H.C., Lelieveldt, B.P.F.: Automated detection of regional wall motion abnormalities based on a statistical model applied to multislice short-axis cardiac MR images. IEEE Trans. Med. Imaging 28(4), 595–607 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Avan Suinesiaputra
    • 1
  • Alejandro F. Frangi
    • 2
  • Theodorus A. M. Kaandorp
    • 1
  • Hildo J. Lamb
    • 1
  • Jeroen J. Bax
    • 3
  • Johan H. C. Reiber
    • 1
  • Boudewijn P. F. Lelieveldt
    • 1
    • 4
  1. 1.Dept. of RadiologyLeiden University of Medical CenterLeidenthe Netherlands
  2. 2.Center of Computational Imaging and Simulation Technologies in BiomedicineUniversitat Pompeu FabraBarcelonaSpain
  3. 3.Dept. of CardiologyLeiden University of Medical CenterLeidenthe Netherlands
  4. 4.Dept. of MediamaticsDelft University of TechnologyDelftthe Netherlands

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