Abstract:

Atrial fibrillation is responsible for a significant and steadily rising burden. Simultaneously, the treatment options for atrial fibrillation are far from optimal. Personalized simulations of cardiac electrophysiology could assist clinicians in the risk stratification and therapy planning for atrial fibrillation. However, the use of personalized simulations in clinics is currently not possible due to either too high computational costs or non-sufficient accuracy. Eikonal simulations come with low computational costs but cannot replicate the influence of cardiac tissue geometry on the conduction velocity of the wave propagation. Consequently, they currently lack the required accuracy to be applied in clinics. Biophysically detailed simulations on the other hand are accurate but associated with too high computational costs. To tackle this issue, a regression model is created based on biophysically detailed bidomain simulation data. This regression formula calculates the conduction velocity dependent on the thickness and curvature of the heart wall. Afterwards the formula was implemented into the eikonal model with the goal to increase the accuracy of the eikonal model without losing its advantage of computational efficiency. The results of the modified eikonal simulations demonstrate that (i) the local activation times become significantly closer to those of the biophysically detailed bidomain simulations, (ii) the advantage of the eikonal model of a low sensitivity to the resolution of the mesh was reduced further, and (iii) the unrealistic occurrence of endo-epicardial dissociation in simulations was remedied. The results suggest that the accuracy of the eikonal model was significantly increased. At the same time, the additional computational costs caused by the implementation of the regression formula are neglectable. In conclusion, a successful step towards a more accurate and fast computational model of cardiac electrophysiology was achieved.

Abstract:

Atrial fibrillation (AFib) is responsible for complications and excess death and its prevalence is expected to further increase in the future, due to the increasing life expectancy of humans and age being a major risk factor [1] [2]. At the same time the treatment options available for AFib are far from optimal. Radio-frequency ablation has the potential to be highly effective with comparably small side effects. The major limitation for the success of radio-frequency ablation is the difficulty to correctly locate the areas responsible for abnormal conduction. Personalized computational models to test strategies for the ablation procedure could be of great benefit for the success rate of this treatment. [3] Bidomain models have the required accuracy to theoretically assist the clinicians, but their high computational cost makes them inapplicable in clinical environments [4]. Eikonal models on the other hand could potentially be used in clinics due to their low computational costs, but lack the required accuracy, because of their inability to capture certain electrophys- iological effects. The goal of this project is to refine the eikonal model by implementing the influence of geometrical factors on conduction velocity (CV) into the eikonal model. This was achieved by using previously published data about the influence of geometrical factors on CV obtained with bidomain simulations to create a regression model. These geo- metrical factors were muscle curvature, muscle thickness and bath loading. The regression model was then implemented into the eikonal model’s local speed function (LSF) aiming to increase the accuracy of the eikonal model. The implementation of the regression formula into the eikonal model was followed by the evaluation of the improvements made by the regression model. This was achieved by creat- ing two dimensional simulation setups to compare the eikonal model before and after the implementation of the regression formula to bidomain simulations. By applying the same stimulus on the same mesh, the eikonal and bidomain simulations became comparable. The muscle tissue model used to create the mesh and run the eikonal and bidomain simulations was a two dimensional rectangle with a variable muscle thickness and the possibility to bend it to different uniform curvatures. The use of this mesh allowed to study the influence of muscle thickness and curvature on the simulations. ....