Pranav Bhagirath

156 Chapter 8 Patient torso Figure 3 shows a multi compartment 3D computer model of a human thorax. The positions of the electrodes on the body surface were derived from the anatomic markers on MRI. Figure 4 demonstrates the resulting 3D mesh. As can be observed the mesh is highly regular and is locally refined in the vicinity of details. Shortest Paths of Activation The computation of all possible shortest paths of the activation wavefront through the cardiac wall resulted in a set of epicardial isochrones yielding a time dependent epicardial potential as shown in figure 5 . Computing BVP Potential equations for 13,000 mesh nodes were solved in 3 seconds utilizing a 2.4 GHz single core. Solving these equations for a mesh consisting of 240,000 nodes, corresponding to a resolution of 5 mm throughout the thorax volume, lasted 3 minutes. Two sequences of computed BVP are shown in figure 6 , one using a mesh edge size of 0.5cm (a-j) and one using a mesh edge size of 1.5cm (k-t). The potential field permeates the lungs without visual deformation, even if their sigma is only half that of their environment. Impact of lung tissue on BVP The impact of variable organ conductivity on BVP was investigated using forward simulations in the human torso model. Figure 7 illustrates the impact on the electric field when a smaller sigma (conductivity) is assigned to lung tissue (A) . The BVP field is compared to simulations in a homogeneous torso model (B) . A smaller sigma of the lung tissue leads to an increased breakthrough of the potential field.