Medical & Biological Engineering & Computing

, Volume 51, Issue 11, pp 1235–1250 | Cite as

Understanding the mechanisms amenable to CRT response: from pre-operative multimodal image data to patient-specific computational models

  • C. Tobon-Gomez
  • N. Duchateau
  • R. Sebastian
  • S. Marchesseau
  • O. Camara
  • E. Donal
  • M. De Craene
  • A. Pashaei
  • J. Relan
  • M. Steghofer
  • P. Lamata
  • H. Delingette
  • S. Duckett
  • M. Garreau
  • A. Hernandez
  • K. S. Rhode
  • M. Sermesant
  • N. Ayache
  • C. Leclercq
  • R. Razavi
  • N. P. Smith
  • A. F. Frangi
Special Issue - Review

Abstract

This manuscript describes our recent developments towards better understanding of the mechanisms amenable to cardiac resynchronization therapy response. We report the results from a full multimodal dataset corresponding to eight patients from the euHeart project. The datasets include echocardiography, MRI and electrophysiological studies. We investigate two aspects. The first one focuses on pre-operative multimodal image data. From 2D echocardiography and 3D tagged MRI images, we compute atlas based dyssynchrony indices. We complement these indices with presence and extent of scar tissue and correlate them with CRT response. The second one focuses on computational models. We use pre-operative imaging to generate a patient-specific computational model. We show results of a fully automatic personalized electromechanical simulation. By case-per-case discussion of the results, we highlight the potential and key issues of this multimodal pipeline for the understanding of the mechanisms of CRT response and a better patient selection.

Keywords

Cardiac resynchronization therapy Dyssynchrony indices Computational models Patient-specific simulation 

Supplementary material

11517_2013_1044_MOESM1_ESM.pptx (163.2 mb)
Supplementary material (pptx 163 MB)

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Copyright information

© International Federation for Medical and Biological Engineering 2013

Authors and Affiliations

  • C. Tobon-Gomez
    • 1
    • 2
    • 3
  • N. Duchateau
    • 1
    • 2
    • 4
  • R. Sebastian
    • 7
  • S. Marchesseau
    • 8
  • O. Camara
    • 1
    • 2
    • 3
  • E. Donal
    • 9
    • 10
    • 11
  • M. De Craene
    • 1
    • 2
    • 5
  • A. Pashaei
    • 1
    • 2
  • J. Relan
    • 8
  • M. Steghofer
    • 1
    • 2
  • P. Lamata
    • 12
    • 13
  • H. Delingette
    • 8
  • S. Duckett
    • 13
  • M. Garreau
    • 9
    • 10
  • A. Hernandez
    • 9
    • 10
  • K. S. Rhode
    • 13
  • M. Sermesant
    • 8
  • N. Ayache
    • 8
  • C. Leclercq
    • 9
    • 10
    • 11
  • R. Razavi
    • 13
  • N. P. Smith
    • 12
    • 13
  • A. F. Frangi
    • 1
    • 2
    • 6
  1. 1.CISTIBUniversitat Pompeu FabraBarcelonaSpain
  2. 2.CIBER-BBNBarcelonaSpain
  3. 3.PhySenseUniversitat Pompeu FabraBarcelonaSpain
  4. 4.Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i SunyerUniversitat de BarcelonaBarcelonaSpain
  5. 5.Philips Research, MedisysSuresnesFrance
  6. 6.Department of Mechanical EngineeringUniversity of SheffieldSheffieldUK
  7. 7.Computational Multi-Scale Physiology Lab Universitat de ValenciaValenciaSpain
  8. 8.INRIA Méditerranée, Asclepios ProjectSophia AntipolisFrance
  9. 9.INSERM, U1099RennesFrance
  10. 10.Université de Rennes 1LTSIRennesFrance
  11. 11.CHU RennesService de Cardiologie et Maladies VasculairesRennesFrance
  12. 12.Department of Computer ScienceUniversity of OxfordOxfordUK
  13. 13.Division of Imaging Sciences and Biomedical EngineeringKing’s College LondonLondonUK

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