Abstract:

Clinical and computational studies highlighted the role of atrial anatomy for atrial fibrillation vulnerability. However, personalized computational models are often generated from electroanatomical maps, which might lack important anatomical structures like the appendages, or from imaging data which are potentially affected by segmentation uncertainty. A bi-atrial statistical shape model (SSM) covering relevant structures for electrophysiological simulations was shown to cover atrial shape variability. We hypothesized that it could, therefore, also be used to infer the shape of missing structures and deliver ready-to-use models to assess atrial fibrillation vulnerability in silico. We implemented a highly automatized pipeline to generate a personalized computational model by fitting the SSM to the clinically acquired geometries. We applied our framework to a geometry coming from an electroanatomical map and one derived from magnetic resonance images (MRI). Only landmarks belonging to the left atrium and no information from the right atrium were used in the fitting process. The left atrium surface-to-surface distance between electroanatomical map and a fitted instance of the SSM was 2.26+-1.95 mm. The distance between MRI segmentation and SSM was 2.07+-1.56 mm and 3.59+-2.84 mm in the left and right atrium, respectively. Our semi-automatic pipeline provides ready-to-use personalized computational models representing the original anatomy well by fitting a SSM. We were able to infer the shape of the right atrium even in the case of using information only from the left atrium.

Abstract:

Although the 3-D anatomy of the heart can be obtained from computed tomography or magnetic resonance imaging, the fiber orientation of cardiac muscle, which significantly affects the electrophysiological characteristics of the heart, can hardly be obtained in vivo. In this work, a highly automated pipeline for annotating fibers and anatomical regions in both atria is introduced. Using k-means clustering, openings of the atria (e.g., pulmonary veins, mitral valve) were identified and given as boundary conditions for solving nine Laplace problems with Dirichlet boundary conditions. A rule-based method was used to derive the fiber orientation and the annotation of the anatomical regions from the Laplace solutions. Three atrial models from different sources were used to benchmark the pipeline. The calculated fiber arrangement was regionally compared by visual inspection and faithfully reproduced clinical and experimental data from literature. Furthermore, a Gaussian process together with a rule-based method is presented that can automatically fit a right atrium for a given real left atrium. Finally the local activation time maps computed from simulations during sinus rhythm were compared to assess the influence of different combinations of left and right atria. It cloud be seen that the fast conduction regions and interatrial bridges have strong influence on the propagation of electrical stimuli.