The goal of ECG imaging is the reconstruction of cardiac electrical sources from the BSPM (body surface potential maps). The tool could have a great clinical potential by providing a cardiologist the quantitative information about the heart condition, thereby enabling pre-interventional planning and facilitating the intervention procedures themselves.
With the knowledge about model geometry, a linear relationship between heart sources and BSPM can be established. The problem is however severely ill-posed, i.e. it is very sensitive to the measurement and modeling errors. Therefore a special mathematical technique, called regularization, should be applied in order to get a stable solution.
In the project new physiologically based approaches to the problem will be developed. In order to overcome the smoothness of a standard Tikhonov solution binary and ternary optimization techniques will be tested for extrasystoles and ischemia regions identification. Furthermore a new spatio-temporal function template of transmembrane voltages will be constructed: at each time step the corresponding functional will be minimized, thus delivering maximum-likelihood estimates for the unknown parameters.
- programming skills in C++, MATLAB
- linear algebra, analysis
- some knowledge about optimization and/or cardiac physiology would be a plus