A Novel Rule-Based Algorithm for Assigning Myocardial Fiber Orientation to Computational Heart Models
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Electrical waves traveling throughout the myocardium elicit muscle contractions responsible for pumping blood throughout the body. The shape and direction of these waves depend on the spatial arrangement of ventricular myocytes, termed fiber orientation. In computational studies simulating electrical wave propagation or mechanical contraction in the heart, accurately representing fiber orientation is critical so that model predictions corroborate with experimental data. Typically, fiber orientation is assigned to heart models based on Diffusion Tensor Imaging (DTI) data, yet few alternative methodologies exist if DTI data is noisy or absent. Here we present a novel Laplace–Dirichlet Rule-Based (LDRB) algorithm to perform this task with speed, precision, and high usability. We demonstrate the application of the LDRB algorithm in an image-based computational model of the canine ventricles. Simulations of electrical activation in this model are compared to those in the same geometrical model but with DTI-derived fiber orientation. The results demonstrate that activation patterns from simulations with LDRB and DTI-derived fiber orientations are nearly indistinguishable, with relative differences ≤6%, absolute mean differences in activation times ≤3.15 ms, and positive correlations ≥0.99. These results convincingly show that the LDRB algorithm is a robust alternative to DTI for assigning fiber orientation to computational heart models.
KeywordsDiffusion tensor Magnetic resonance Imaging Finite element Mesh Laplace–Dirichlet
The authors would like to thank Dr. Edward Vigmond at the University of Bordeaux for his software Meshalyzer. This work was supported by grants AHA 10PRE3650037 to Jason Bayer, NIH R01 HL082729 and HL103428, and NSF CBET-0933029 to Natalia Trayanova, and FWF F3210-N18 and NIH R01 HL10119601 to Gernot Plank.
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