A Novel Rule-Based Algorithm for Assigning Myocardial Fiber Orientation to Computational Heart Models
By J. Bayer, R. C. Blake, Gernot Plank, Natalia A. Trayanova. 2012
561 citations recorded by the source corpus.
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.
Subfields: Cardiology and Cardiovascular Medicine, Computational Mathematics, Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- Physics-Informed Neural Networks for Cardiac Activation Mapping (2020, methodology): “There are several methodologies to incorporate this information with ruled-based approaches [38, 39] and mapping techniques [40, 41].”
- MFEM: a modular finite element methods library (2019, methodology): “The methods implemented are outlined in [89] and [90].”
- Inverse localization of earliest cardiac activation sites from activation maps based on the viscous Eikonal equation (2019, methodology): “4.3 will have constant velocities v f = 0.6m/s, vs = 0.4m/s, vn = 0.2m/s and heterogeneous vectors { f , s, n}, computed by a rule-based method (Bayer et al. 2012).”
- Automatic mapping of atrial fiber orientations for patient‐specific modeling of cardiac electromechanics using image registration (2018, unknown): “Due to individual fiber bundles running in different directions it is not straightforward to use rule-based approaches, as it can be done in the ventricles [8], [9].”