The interaction of spiral waves of excitation with atrial anatomy remains unclear. This simulation study isolates the role of atrial anatomical structures on spiral wave spontaneous drift in the human atrium. We implemented realistic and idealised 3D human atria models to investigate the functional impact of anatomical structures on the long-term ( approximately 40 s) behaviour of spiral waves. The drift of a spiral wave was quantified by tracing its tip trajectory, which was correlated to atrial anatomical features. The interaction of spiral waves with the following idealised geometries was investigated: (a) a wedge-like structure with a continuously varying atrial wall thickness; (b) a ridge-like structure with a sudden change in atrial wall thickness; (c) multiple bridge-like structures consisting of a bridge connected to the atrial wall. Spiral waves drifted from thicker to thinner regions and along ridge-like structures. Breakthrough patterns caused by pectinate muscles (PM) bridges were also observed, albeit infrequently. Apparent anchoring close to PM-atrial wall junctions was observed. These observations were similar in both the realistic and the idealised models. We conclude that spatially altering atrial wall thickness is a significant cause of drift of spiral waves. PM bridges cause breakthrough patterns and induce transient anchoring of spiral waves.
Atrial fibrillation (AF) is a common cardiac disease of genuine clinical concern with high rates of morbidity, leading to major personal and National Health Service costs. Computer modelling of AF using biophysically detailed cellular models with realistic 3D anatomical geometry allows investigation of the underlying ionic mechanisms in far more detail than with experimental physiology. We have developed a 3D virtual human atrium that combines detailed cellular electrophysiology including ion channel kinetics and homeostasis of ionic concentrations with anatomical details. The segmented anatomical structure and the multi- variable nature of the system make the 3D simulations of AF computationally large and intensive.
Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. The atrial wall thickness (AWT) can potentially improve our understanding of the mechanism underlying atrial structure that drives AF and provides important clinical information. However, most existing studies for estimating AWT rely on ruler-based measurements performed on only a few selected locations in 2D or 3D using digital calipers. Only a few studies have developed automatic approaches to estimate the AWT in the left atrium, and there are currently no methods to robustly estimate the AWT of both atrial chambers. Therefore, we have developed a computational pipeline to automatically calculate the 3D AWT across bi-atrial chambers and extensively validated our pipeline on both ex vivo and in vivo human atria data. The atrial geometry was first obtained by segmenting the atrial wall from the MRIs using a novel machine learning approach. The epicardial and endocardial surfaces were then separated using a multi-planar convex hull approach to define boundary conditions, from which, a Laplace equation was solved numerically to automatically separate bi-atrial chambers. To robustly estimate the AWT in each atrial chamber, coupled partial differential equations by coupling the Laplace solution with two surface trajectory functions were formulated and solved. Our pipeline enabled the reconstruction and visualization of the 3D AWT for bi-atrial chambers with a relative error of 8% and outperformed existing algorithms by >7%. Our approach can potentially lead to improved clinical diagnosis, patient stratification, and clinical guidance during ablation treatment for patients with AF.
Atrial fibrillation (AF) induced electrical remodelling of ionic channels shortens action potential duration and reduces atrial excitability. Experimental data of AF-induced electrical remodelling (AFER) from two previous studies on human atrial myocytes were incorporated into a human atrial cell computer model to simulate their effects on atrial electrical behaviour. The dynamical behaviors of excitation scroll waves in an anatomical 3D homogenous model of human atria were studied for control and AF conditions. Under control condition, scroll waves meandered in large area and became persistent when entrapped by anatomical obstacles. In this case, a mother rotor dominated atrial excitation. Action potentials from several sites behaved as if the atrium were paced rapidly. Under AF conditions, AFER increased the stability of re-entrant scroll waves by reducing meander. Scroll wave break up leads to wavelets underpinning sustained chronic AF. Our simulation results support the hypothesis that AF-induced electrical remodelling perpetuates and sustains AF.
Atrial fibrillation (AF) is a common cardiac disease with high rates of morbidity, leading to major personal and NHS costs. Computer modeling of AF using a detailed cellular model with realistic 3D anatomical geometry allows investigation of the underlying ionic mechanisms in far more detail than in a physiology laboratory. We have developed a 3D virtual human atrium that combines detailed cellular electrophysiology, including ion channel kinetics and homeostasis of ionic concentrations, with anatomical detail. The segmented anatomical structure and multi-variable nature of the system makes the 3D simulations of AF large and computationally intensive. The computational demands are such that a full problem solving environment requires access to resources of High Performance Computing (HPC), High Performance Visualization (HPV), remote data repositories and a backend infrastructure. This is a classic example of eScience and Gridenabled computing. Initial work has been carried out using multiple processor machines with shared memory architectures. As spatial resolution of anatomical models increases, requirement of HPC resources is predicted to increase many-fold ( ~ 1 10 teraflops). Distributed computing is essential, both through massively parallel systems (a single supercomputer) and multiple parallel systems made accessible through the Grid.