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A 3D MRI-Based Cardiac Computer Model to Study Arrhythmia and Its In-vivo Experimental Validation

  • Mihaela Pop
  • Maxime Sermesant
  • Jean-Marc Peyrat
  • Eugene Crystal
  • Sudip Ghate
  • Tommaso Mansi
  • Ilan Lashevsky
  • Beiping Qiang
  • Elliot R. McVeigh
  • Nicholas Ayache
  • Graham A. Wright
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6666)

Abstract

The aim of this work was to develop a simple and fast 3D MRI-based computer model of arrhythmia inducibility in porcine hearts with chronic infarct scar, and to further validate it using electrophysiology (EP) measures obtained in-vivo. The heart model was built from MRI scans (with voxel size smaller than 1mm3) and had fiber directions extracted from diffusion tensor DT-MRI. We used a macroscopic model that calculates the propagation of action potential (AP) after application of a train of stimuli, with location and timing replicating precisely the stimulation protocol used in the in-vivo EP study. Simulation results were performed for two infarct hearts: one with non-inducible and the other with inducible ventricular tachycardia (VT), successfully predicting the study outcome like in the in-vivo cases; for the inducible heart, the average predicted VT cycle length was 273ms, compared to a recorded VT of approximately 250ms. We also generated synthetic fibers for each heart and found the associated helix angle whose transmural variation (in healthy zones) from endo- to epicardium gave the smallest difference (i.e., approx. 41°) when compared to the helix angle corresponding to fibers from DW-MRI. Mean differences between activation times computed using DT-MRI fibers and using synthetic fibers for the two hearts were 6 ms and 11 ms, respectively.

Keywords

electrophysiology computer modelling cardiac MR imaging 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mihaela Pop
    • 1
  • Maxime Sermesant
    • 2
    • 3
  • Jean-Marc Peyrat
    • 4
  • Eugene Crystal
    • 1
  • Sudip Ghate
    • 1
  • Tommaso Mansi
    • 5
  • Ilan Lashevsky
    • 1
  • Beiping Qiang
    • 1
  • Elliot R. McVeigh
    • 6
  • Nicholas Ayache
    • 2
  • Graham A. Wright
    • 1
  1. 1.Sunnybrook Research InstituteUniv. of TorontoCanada
  2. 2.INRIA (Sophia-Antipolis)France
  3. 3.Div. Imaging ScienceKCLLondonUK
  4. 4.Siemens Molecular ImagingOxfordUK
  5. 5.Siemens Corporate ResearchPrincetonUSA
  6. 6.Johns Hopkins UniversityBaltimoreUSA

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