Advertisement

A Rule-Based Method to Model Myocardial Fiber Orientation for Simulating Ventricular Outflow Tract Arrhythmias

  • Rubén DosteEmail author
  • David Soto-Iglesias
  • Gabriel Bernardino
  • Rafael Sebastian
  • Sophie Giffard-Roisin
  • Rocio Cabrera-Lozoya
  • Maxime Sermesant
  • Antonio Berruezo
  • Damián Sánchez-Quintana
  • Oscar Camara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10263)

Abstract

Myocardial fiber orientation determines the propagation of electrical waves in the heart and the contraction of cardiac tissue. One common approach for assigning fiber orientation to cardiac anatomical models are Rule-Based Methods (RBM). However, RBM have been developed to assimilate data mostly from the Left Ventricle. In consequence, fiber information from RBM does not match with histological data in other areas of the heart, having a negative impact in cardiac simulations beyond the LV. In this work, we present a RBM where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the right ventricle endocardium, the interventricular septum and the outflow tracts. Electrophysiological simulations including these anatomical structures were then performed, with patient-specific data of outflow tract ventricular arrhythmias (OTVA) cases. A comparison between the obtained simulations and electro-anatomical data of these patients confirm the potential for in silico identification of the site of origin in OTVAs before the intervention.

Keywords

Fiber orientation Rule-based method Electrophysiological simulations Arrhythmias Outflow tracts 

References

  1. 1.
    Arevalo, H.J., et al.: Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nat. Commun. 7, 11437 (2016)CrossRefGoogle Scholar
  2. 2.
    Lombaert, H., et al.: Human atlas of the cardiac fiber architecture: study on a healthy population. IEEE Trans. Med. Imaging 31(7), 1436–1447 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Young, R.J., Panfilov, A.V.: Anisotropy of wave propagation in the heart can be modeled by a Riemannian electrophysiological metric. Proc. Natl. Acad. Sci. U.S.A. 107(34), 15063–15068 (2010)CrossRefGoogle Scholar
  4. 4.
    Hooks, D.A., et al.: Laminar arrangement of ventricular myocytes influences electrical behavior of the heart. Circ. Res. 101(10), 103–113 (2007)CrossRefGoogle Scholar
  5. 5.
    Bayer, J.D., et al.: A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models. Ann. Biomed. Eng. 40(10), 2243–2254 (2012)CrossRefGoogle Scholar
  6. 6.
    Agger, P., et al.: Insights from echocardiography, magnetic resonance imaging, and microcomputed tomography relative to the mid-myocardial left ventricular echogenic zone. Echocardiography 33(10), 1546–1556 (2016)CrossRefGoogle Scholar
  7. 7.
    Boettler, P., et al.: New aspects of the ventricular septum and its function: an echocardiographic study. Heart 91(10), 1343–1348 (2005)CrossRefGoogle Scholar
  8. 8.
    Kocica, M.J., et al.: The helical ventricular myocardial band: global, three-dimensional, functional architecture of the ventricular myocardium. Eur. J. Cardio-thoracic Surg. 29(SUPPL.), 1 (2006)Google Scholar
  9. 9.
    Streeter, D.D., et al.: Fiber orientation in the canine left ventricle during diastole and systole. Circ. Res. 24(3), 339–347 (1969)CrossRefGoogle Scholar
  10. 10.
    Greenbaum, R.A., et al.: Left ventricular fibre architecture in man. Br. Heart J. 45(1980), 248–263 (1981)CrossRefGoogle Scholar
  11. 11.
    Sanchez-Quintana, D., et al.: Anatomical basis for the cardiac interventional electrophysiologist. BioMed. Res. Int. Ao. 2015 (2015)Google Scholar
  12. 12.
    Talbot, H., et al.: Towards an interactive electromechanical model of the heart. Interface Focus 3(2) (2013)Google Scholar
  13. 13.
    Acosta, J., et al.: Impact of earliest activation site location in the septal right ventricular outflow tract for identification of left vs right outflow tract origin of idiopathic ventricular arrhythmias. Heart Rhythm 12(4), 726–734 (2015)CrossRefGoogle Scholar
  14. 14.
    Herczku, C., et al.: Mapping data predictors of a left ventricular outflow tract origin of idiopathic ventricular tachycardia with V3 transition and septal earliest activation. Circ. Arrhythm. Electrophysiol. 5(3), 484–491 (2012)CrossRefGoogle Scholar
  15. 15.
    Toussaint, N., et al.: In vivo human cardiac fibre architecture estimation using shape-based diffusion tensor processing. Med. Image Anal. 17(8), 1243–1255 (2013)CrossRefGoogle Scholar
  16. 16.
    Mekkaoui, C., et al.: Diffusion tractography of the entire left ventricle by using free-breathing accelerated simultaneous multisection imaging. Radiology, 152613 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Rubén Doste
    • 1
    Email author
  • David Soto-Iglesias
    • 1
  • Gabriel Bernardino
    • 1
  • Rafael Sebastian
    • 2
  • Sophie Giffard-Roisin
    • 3
  • Rocio Cabrera-Lozoya
    • 3
  • Maxime Sermesant
    • 3
  • Antonio Berruezo
    • 4
  • Damián Sánchez-Quintana
    • 5
  • Oscar Camara
    • 1
  1. 1.PhysenseUniversitat Pompeu FabraBarcelonaSpain
  2. 2.Computational Multiscale Simulation Lab (CoMMLab), Department of Computer ScienceUniversitat de ValenciaValenciaSpain
  3. 3.Asclepios Research GroupInriaSophia-AntipolisFrance
  4. 4.Arrhythmia Section, Cardiology Department, Thorax Institute, Hospital ClinicUniversity of BarcelonaBarcelonaSpain
  5. 5.Department of Anatomy and Cell Biology, Faculty of MedicineUniversity of ExtremaduraBadajozSpain

Personalised recommendations