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Steps Towards Quantification of the Cardiological Stress Exam

  • R. ChabiniokEmail author
  • E. Sammut
  • M. Hadjicharalambous
  • L. Asner
  • D. Nordsletten
  • R. Razavi
  • N. Smith
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9126)

Abstract

In this work we aim to advance the translation of model-based myocardial contractility estimation to the clinical problem of quantitative assessment of the dobutamine stress exam. In particular, we address the question of limited spatial resolution of the observations obtained from cine MRI during the stress test, in which typically only a small number of cine MRI slices are acquired. Due to the relative risk during the dobutamine infusion, a safe acquisition protocol with a healthy volunteer under the infusion of a beta-blocker is applied in order to get a better insight into the contractility estimation using such a type of clinical data. The estimator is compared for three types of observations, namely the processed short axis cine stack contiguously covering the ventricles, the short axis stack limited to only 3 slices and the combination of 3 short and 3 long axis slices. A decrease of contractilities in AHA regions under the beta-blocker infusion was estimated for each observation. The corrected model (by using the estimated parameters) was then compared with the displacements extracted from 3D tagged MRI.

Keywords

Dobutamine Stress Regional Wall Motion Abnormality Cine Magnetic Resonance Imaging Epicardial Surface Short Axis Cine 
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.

Notes

Acknowledgments

The authors acknowledge the support of Engineering and Physical Sciences Research Council EP/H046410/1, British heart foundation grant NH/11/5/29058 and Cardiovascular Healthcare Technology Cooperative. In addition, the author are thankful to P. Moireau and D. Chapelle (Inria, France) for providing the HeartLab software library, used in this work for all modeling and estimation computations, and to D. Rueckert and W. Shi (Imperial College London, Ixico) for providing the IRTK based motion tracking and valuable discussions. This research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St. Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

References

  1. 1.
    Caruel, M., Chabiniok, R., Moireau, P., Lecarpentier, Y., Chapelle, D.: Dimensional reductions of a cardiac model for effective validation and calibration. Biomech. Model. Mechanobiol. 13(4), 897–914 (2014)CrossRefGoogle Scholar
  2. 2.
    Chabiniok, R., Bhatia, K.K., King, A.P., Rueckert, D., Smith, N.: Manifold learning for cardiac modeling and estimation framework. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2014. LNCS, vol. 8896, pp. 284–294. Springer, Heidelberg (2015) Google Scholar
  3. 3.
    Chabiniok, R., Moireau, P., Lesault, P.-F., Rahmouni, A., Deux, J.-F., Chapelle, D.: Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model. Biomech. Model. Mechanobiol. 11(5), 609–630 (2012)CrossRefGoogle Scholar
  4. 4.
    Chapelle, D., Fragu, M., Mallet, V., Moireau, P.: Fundamental principles of data assimilation underlying the Verdandi library: applications to biophysical model personalization within euHeart. Med. Biol. Eng. Comput. 51(11), 1221–1233 (2013)CrossRefGoogle Scholar
  5. 5.
    Chapelle, D., Le Tallec, P., Moireau, P., Sorine, M.: An energy-preserving muscle tissue model: formulation and compatible discretizations. Int. J. Multiscale Comput. Eng. 10(2), 189–211 (2012)CrossRefGoogle Scholar
  6. 6.
    Imperiale, A., Chabiniok, R., Moireau, P., Chapelle, D.: Constitutive parameter estimation methodology using tagged-MRI data. In: Metaxas, D.N., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 409–417. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  7. 7.
    Jahnke, C., Nagel, E., Gebker, R., Kokocinski, T., Kelle, S., Manka, R., Fleck, E., Paetsch, I.: Prognostic value of cardiac magnetic resonance stress tests: adenosine stress perfusion and dobutamine stress wall motion imaging. Circulation 115, 1769–1776 (2007)CrossRefGoogle Scholar
  8. 8.
    Moireau, P., Chapelle, D.: Reduced-order unscented Kalman filtering with application to parameter identification in large-dimensional systems. ESAIM Control Optimisation Calc. Var. 17, 380–405 (2011)zbMATHMathSciNetCrossRefGoogle Scholar
  9. 9.
    Moireau, P., Chapelle, D., Le Tallec, P.: Joint state and parameter estimation for distributed mechanical systems. Comput. Methods Appl. Mech. Eng. 197, 659–677 (2008)zbMATHCrossRefGoogle Scholar
  10. 10.
    Moireau, P., Chapelle, D., Le Tallec, P.: Filtering for distributed mechanical systems using position measurements: perspectives in medical imaging. Inverse Problems, 25(3):035010, p. 25 (2009)Google Scholar
  11. 11.
    Shi, W., Zhuang, X., Wang, H., Duckett, S., Luong, D.V.N., Tobon-Gomez, C., Tung, K., Edwards, P., Rhode, K., Razavi, R., Ourselin, S., Rueckert, D.: A comprehensive cardiac motion estimation framework using both untagged and 3D tagged MR images based on non-rigid registration. IEEE Trans. Med. Imaging 31(6), 1263–1275 (2012)CrossRefGoogle Scholar
  12. 12.
    Wang, V.Y., Lam, H.I., Ennis, D.B., Cowan, B.R., Young, A.A., Nash, M.P.: Modelling passive diastolic mechanics with quantitative MRI of cardiac structure and function. Med. Image Anal. 13(5), 773–784 (2009)CrossRefGoogle Scholar
  13. 13.
    Xi, J., Lamata, P., Niederer, S., Land, S., Shi, W., Zhuang, X., Ourselin, S., Duckett, S., Shetty, A., Rinaldi, C., Rueckert, D., Razavi, R., Smith, N.: The estimation of patient-specific cardiac diastolic functions from clinical measurements. Med. Image Anal. 17(2), 133–146 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • R. Chabiniok
    • 1
    • 2
    Email author
  • E. Sammut
    • 2
  • M. Hadjicharalambous
    • 2
  • L. Asner
    • 2
  • D. Nordsletten
    • 2
  • R. Razavi
    • 2
  • N. Smith
    • 2
  1. 1.Inria Saclay Ile-de-France, MΞDISIM TeamPalaiseauFrance
  2. 2.Division of Imaging Sciences and Biomedical Engineering, St Thomas’ HospitalKing’s College LondonLondonUK

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