Patient-specific modeling of left ventricular electromechanics as a driver for haemodynamic analysis

Abstract

Aims: Models of blood flow in the left ventricle (LV) and aorta are an important tool for analysing the interplay between LV deformation and flow patterns. Typically, image-based kinematic models describing endocardial motion are used as an input to blood flow simulations. While such models are suitable for analysing the hemodynamic status quo, they are limited in predicting the response to interventions that alter afterload conditions. Mechano-fluidic models using biophysically detailed electromechanical (EM) models have the potential to overcome this limitation, but are more costly to build and compute. We report our recent advancements in developing an automated workflow for the creation of such CFD ready kinematic models to serve as drivers of blood flow simulations.

Methods and results: EM models of the LV and aortic root were created for four pediatric patients treated for either aortic coarctation or aortic valve disease. Using MRI, ECG and invasive pressure recordings, anatomy as well as electrophysiological, mechanical and circulatory model components were personalized.

Results: The implemented modeling pipeline was highly automated and allowed model construction and execution of simulations of a patient's heartbeat within 1 day. All models reproduced clinical data with acceptable accuracy.

Conclusion: Using the developed modeling workflow, the use of EM LV models as driver of fluid flow simulations is becoming feasible. While EM models are costly to construct, they constitute an important and nontrivial step towards fully coupled electro-mechano-fluidic (EMF) models and show promise as a tool for predicting the response to interventions which affect afterload conditions.

Keywords: Computer model; Left ventricular electromechanics; Personalization.

Figure 1

Illustration of the various stages of the anatomical model building workflow.

Figure 2

LV model: (A) chosen sites of earliest activation at anatomical locations of the septal ($x_{\text{SF}}$), anterior ($x_{\text{AF}}$) and posterior ($x_{\text{PF}}$) fascicles. (B) Simulated activation sequence. (C) Comparison of computed (dashed line) and measured (solid line) ECGs. BiV model: (D) simulated activation sequence for the BiV (top) and reduced LV (bottom) model. (E) Influence of the RV on the ECG. Computed ECGs are shown for the BiV (blue) and the LV (red) models.

Figure 3

(A) Representative anatomical model showing the mechanical boundary conditions. The end of the aortic root (yellow) and the end of a soft material block attached to the apex of the LV (blue) are fixed in space (purple). Outflow from the ventricle is regulated by a three-element Windkessel model (shown as a representative circuit). (B) Personalized anatomical models and pressure–volume (PV) loops for four patients.

Figure 4

Validation of model predicted strain against MRI. (A) A slice of the model equivalent to the plane acquired in tagged MRI was extracted, and circumferential strain in this slice was evaluated from the model predicted deformations. (B) Model predicted circumferential strain was averaged over the slice and plotted over systole (red). These strains were compared with strains evaluated from tagged MRI (blue). This validation was performed for cases 3 and 4 only, as no tagged MRI was acquired in cases 1 and 2.

Figure 5

Transmural variability of fiber strain at varying depths from the epi- (0%) to endocardium (100%) along a transmural section of the mid lateral LV wall.