Noninvasive electrical imaging of the heart: theory and model development.
The aim of this work is to begin quantifying the performance of a recently developed activation imaging algorithm of Huiskamp and Greensite [IEEE Trans. Biomed. Eng. 44:433-446]. We present here the modeling and computational issues associated with this process. First, we present a practical construction of the appropriate transfer matrix relating an activation sequence to body surface potentials from a general boundary value problem point of view. This approach makes explicit the role of different Green's functions and elucidates features (such as the anisotropic versus isotropic distinction) not readily apparent from alternative formulations. A new analytic solution is then developed to test the numerical implementation associated with the transfer matrix formulation presented here and convergence results for both potentials and normal currents are given. Next, details of the construction of a generic porcine model using a nontraditional data-fitting procedure are presented. The computational performance of this model is carefully examined to obtain a mesh of an appropriate resolution to use in inverse calculations. Finally, as a test of the entire approach, we illustrate the activation inverse procedure by reconstructing a known activation sequence from simulated data. For the example presented, which involved two ectopic focii with large amounts of Gaussian noise (100 microV rms) present in the torso signals, the reconstructed activation sequence had a similarity index of 0.880 when compared to the input source.
Pullan
A J
AJ
Department of Engineering Science, The University of Auckland, New Zealand. a.pullan@auckland.ac.nz
Cheng
L K
LK
Nash
M P
MP
Bradley
C P
CP
Paterson
D J
DJ
D000465:Algorithms; D000818:Animals; D018780:Body Surface Potential Mapping / Q000379:methods; D003198:Computer Simulation; D004553:Electric Conductivity; D006321:Heart / Q000502:physiology*; D008955:Models, Cardiovascular; D019047:Phantoms, Imaging; D012815:Signal Processing, Computer-Assisted; D013552:Swine