The functional impact of myofiber macroscopic organization and disarray in computational models of the murine heart

Abstract

A major challenge in computational models of cardiac electromechanics is the reconstruction of myocardial fiber architecture, as direct in vivo measurements of fiber orientation are not feasible. Consequently, rule-based methods are commonly adopted as surrogates. This study investigates the respective roles of macroscopic fiber architecture and microscopic fiber disarray in cardiac electromechanical simulations. A high-fidelity biventricular electromechanical model of a murine heart was developed using a high-resolution myocardial fiber field obtained via mesoscopic optical imaging, which serves as a reference ground truth. A spatial smoothing strategy is introduced to decouple macroscopic fiber organization from local disarray, and the resulting responses are also compared with those obtained using a rule-based fiber field. The results show that passive mechanics and electrophysiological activation are only weakly affected by fiber disarray, with global chamber compliance and activation times remaining largely unchanged across different fiber descriptions. In contrast, active mechanics is highly sensitive to fiber architecture. Moderate regularization of the experimentally measured fiber field enhances the ventricular pumping efficiency of the computational model by reducing microscopic disarray while preserving the macroscopic helical organization, whereas excessive smoothing or rule-based fiber reconstructions lead to unphysiologically strong or inefficient contraction. Within this framework, two commonly adopted surrogate strategies to account for fiber disarray are investigated: a reduction of the effective cross-bridge stiffness in the active tension model, and the introduction of controlled misalignment between active tension and the local fiber direction. Overall, the results reveal important limitations of commonly adopted surrogate approaches for modeling fiber disarray.

Figures (16)

• Figure 1: From myocardial to fiber reference . (a) Rotation of $\mathbf{e}_{\mathrm{l}}$ by an angle $\alpha$ around the axis $\mathbf{e}_t$ to obtain $\mathbf{f}_{\mathrm{LN}}$, followed by a rotation of $\mathbf{f}_{\mathrm{LN}}$ by an angle $\gamma$ in the plane $\text{span}\{\mathbf{f}_{\mathrm{LN}}, \mathbf{e}_t \}$ (depicted in blue) to obtain $\mathbf{f}$. (b) Rotation of $\mathbf{e}_t$ by an angle $\gamma$ to obtain $\mathbf{e}_t'$. (c) Rotation of $\mathbf{e}_t'$ by an angle $\beta$ around $\mathbf{f}$ (in the lilac plane) to obtain $\mathbf{s}$, and definition of $\mathbf{n}$ as the normal to both $\mathbf{f}$ and $\mathbf{s}$.
• Figure 2: Construction of the myocardial reference system. (a) apico-basal distance $\psi$ (b) transmural distance $\phi$ , (c) myocardial reference system.
• Figure 3: Comparison different fiber fields derived by varying the regularization radius $\ell$ with respect to the the unfiltered fiber field and the ldrbm one. On the left the unfiltered fiber field and on the right the ldrbm one doste2019rule. The colormap represents the angular variation of $\alpha$ within the myocardium.
• Figure 4: Angles mapping. Variation of $\alpha$ and $\gamma$ angles within the ventricular sub-regions.
• Figure 5: Angles distribution. Distribution of the $\alpha$ and $\gamma$ angles in different zones of the domain.