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Computational Heart Modeling for Evaluating Efficacy of MRI Techniques in Predicting Appropriate ICD Therapy

  • Eranga Ukwatta
  • Plamen Nikolov
  • Natalia Trayanova
  • Graham Wright
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

The objective of this study is to use individualized heart computer models in evaluating efficacy of myocardial infarct (MI) mass determined by two different MRI techniques in predicting patient risk for post-MI ventricular tachycardia (VT). 27 patients with MI underwent late gadolinium-enhanced MRI using inversion-recovery fast gradient echo (IR-FGRE) and multi-contrast late enhancement (MCLE) prior to implantable cardioverter defibrillators (ICD) implantation and were followed up for 6–46 months. The myocardium, MI core (IC), and border zone (BZ) were segmented from the images using previously validated techniques. The segmented structures were then reconstructed as a high-resolution label map in 3D. Individualized image-based computational models were built separately for each imaging technique; simulations of propensity to VT were conducted with each model. The imaging methods were evaluated for sensitivity and specificity by comparing simulated inducibility of VT to clinical outcome (appropriate ICD therapy) in patients. Twelve patients had at least one appropriate ICD therapy for VT at follow-up. For both MCLE and IR-FGRE, the outcomes of the simulations of VT were significantly different between the groups with and without ICD therapy. Between the IR-FGRE and MCLE, the virtual models built using the latter may have yielded higher sensitivity and specificity in predicting appropriate ICD therapy.

Keywords

T1 mapping Computational modeling Myocardial infarct 

References

  1. 1.
    Fishman, G.I., et al.: Sudden cardiac death prediction and prevention: Report from a national heart, lung, and blood institute and heart rhythm society workshop. Circulation 122(22), 2335–2348 (2010)CrossRefGoogle Scholar
  2. 2.
    Ismail, T.F., Prasad, S.K., Pennell, D.J.: Prognostic importance of late gadolinium enhancement cardiovascular magnetic resonance in cardiomyopathy. Heart 98(6), 438–442 (2012)CrossRefGoogle Scholar
  3. 3.
    Schmidt, A., et al.: Infarct tissue heterogeneity by magnetic resonance imaging identifies enhanced cardiac arrhythmia susceptibility in patients with left ventricular dysfunction. Circulation 115(15), 2006–2014 (2007)CrossRefGoogle Scholar
  4. 4.
    Yang, Y., et al.: Multi-contrast late enhancement CMR determined gray zone and papillary muscle involvement predict appropriate ICD therapy in patients with ischemic heart disease. J. Cardiovasc. Magn. Reson. 15(1), 57 (2013)CrossRefGoogle Scholar
  5. 5.
    Arevalo, H.J., et al.: Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nat. Commun. 7, 11437 (2016)CrossRefGoogle Scholar
  6. 6.
    Trayanova, N.A., Pashakhanloo, F., Wu, K.C., Halperin, H.R.: Imaging-based simulations for predicting sudden death and guiding ventricular tachycardia ablation. Circ. Arrhythm. Electrophysiol. 10(7), e004743 (2017)CrossRefGoogle Scholar
  7. 7.
    Vadakkumpadan, F., Arevalo, H., Jebb, A., Wu, K.C., Trayanova, N.: Image-based patient-specific simulations of ventricular electrophysiology for sudden arrhythmic death risk stratification. In: Circulation, vol. 128, no. 22 (2013)Google Scholar
  8. 8.
    Ukwatta, E., et al.: Myocardial infarct segmentation from magnetic resonance images for personalized modeling of cardiac electrophysiology. IEEE TMI 35(6), 1408–1419 (2016)Google Scholar
  9. 9.
    Detsky, J.S., Paul, G., Dick, A.J., Wright, G.A.: Reproducible classification of infarct heterogeneity using fuzzy clustering on multicontrast delayed enhancement magnetic resonance images. IEEE TMI 28(10), 1606–1614 (2009)Google Scholar
  10. 10.
    Bayer, J.D.J., Blake, R.C.R., Plank, G., Trayanova, N.A.N.A.: A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models. Ann. Biomed. Eng. 40(10), 2243–2254 (2012)CrossRefGoogle Scholar
  11. 11.
    Plank, G., et al.: From mitochondrial ion channels to arrhythmias in the heart: computational techniques to bridge the spatio-temporal scales. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 366(1879), 3381–3409 (2008)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Moreno, J.D., et al.: A computational model to predict the effects of class I anti-arrhythmic drugs on ventricular rhythms. Sci. Trans. Med. 3(98), 98ra83 (2011)CrossRefGoogle Scholar
  13. 13.
    ten Tusscher, K.H.W.J., Noble, D., Noble, P.J., Panfilov, A.V.: A model for human ventricular tissue. Am. J. Physiol. Circ. Physiol. 286(4), H1573–H1589 (2004)CrossRefGoogle Scholar
  14. 14.
    Wellens, H.J.J., Brugada, P., Stevenson, W.G.: Programmed electrical stimulation of the heart in patients with life-threatening ventricular arrhythmias: what is the significance of induced arrhythmias and what is the correct stimulaton protocol? Circulation 72(1), 1–7 (1985)CrossRefGoogle Scholar
  15. 15.
    Arevalo, H., Plank, G., Helm, P., Halperin, H., Trayanova, N.: Tachycardia in post-infarction hearts: insights from 3D image-based ventricular models. PLoS ONE 8(7), e68872 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Eranga Ukwatta
    • 1
    • 2
  • Plamen Nikolov
    • 3
  • Natalia Trayanova
    • 3
  • Graham Wright
    • 4
  1. 1.Systems and Computer EngineeringCarleton UniversityOttawaCanada
  2. 2.School of EngineeringUniversity of GuelphGuelphCanada
  3. 3.Institute of Computational MedicineJohns Hopkins UniversityBaltimoreUSA
  4. 4.Department of Medical BiophysicsUniversity of TorontoTorontoCanada

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