Construction of boundary element models from magnetic resonance images for bioelectromagnetic inverse problems

Multisensor electroand magnetoencephalographic (EEG and MEG) as well as electroand magnetocardiographic (ECG and MCG) recordings have been proved useful in noninvasively exctracting information on bioelectric excitation [2, 6]. Since the body affects, i.e. filters, the electromagnetic signals measured outside of the body, the anatomy of the patient need to be taken into account, when excitation sites are localized by solving the inverse problem. Boundary element method (BEM) is a widely adopted technique, where the body is modeled as an inhomogenous volume conductor presented by surfaces, such as scalp, skull and brain in encephalic applications, or thorax, lungs and heart surfaces in cardiac applications. The anatomy of the patient has to be individually modeled, if accurate high-quality inverse solutions are desired. Modern medical imaging modalities, such as magnetic resonance (MR) imaging and computerized tomography (CT), provide detailed three-dimensional (3-D) anatomic information. MR imaging has established its position as a standard method to retrieve anatomic information for bioelectromagnetic modeling. Nevertheless, various solutions to the model construction, in the context of bioelectromagnetic problems, are not widely reported. A few procedures, more or less automated and flexible, have been, however, described [1, 9, 10]. This work presents a methodology to construct individualized boundary element (BE) models for bioelectromagnetic inverse problems. The objective of the work was to develop methods and software, which construct boundary element models as automatically and fast as possible. This work focus on cardiac problems, but the modeling of the head is also addressed.