Construction of boundary element models in bioelectromagnetism

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49, [55]
Multisensor electro- and magnetoencephalographic (EEG and MEG) as well as electro- and magnetocardiographic (ECG and MCG) recordings have been proved useful in noninvasively extracting information on bioelectric excitation. The anatomy of the patient needs to be taken into account, when excitation sites are localized by solving the inverse problem. In this work, a methodology has been developed to construct patient specific boundary element models for bioelectromagnetic inverse problems from magnetic resonance (MR) data volumes as well as from two orthogonal X-ray projections. The process consists of three main steps: reconstruction of 3-D geometry, triangulation of reconstructed geometry, and registration of the model with a bioelectromagnetic measurement system. The 3-D geometry is reconstructed from MR data by matching a 3-D deformable boundary element template to images. The deformation is accomplished as an energy minimization process consisting of image and model based terms. The robustness of the matching is improved by multi-resolution and global-to-local approaches as well as using oriented distance maps. A boundary element template is also used when 3-D geometry is reconstructed from X-ray projections. The deformation is first accomplished in 2-D for the contours of simulated, built from the template, and real X-ray projections. The produced 2-D vector field is back-projected and interpolated on the 3-D template surface. A marching cube triangulation is computed for the reconstructed 3-D geometry. Thereafter, a non-iterative mesh-simplification method is applied. The method is based on the Voronoi-Delaunay duality on a 3-D surface with discrete distance measures. Finally, the triangulated surfaces are registered with a bioelectromagnetic measurement utilizing markers. More than fifty boundary element models have been successfully constructed from MR images using the methods developed in this work. A simulation demonstrated the feasibility of X-ray reconstruction; some practical problems of X-ray imaging need to be solved to begin tests with real data.
segmentation, triangulation, 3-D reconstruction, registration
  • J. Lötjönen, P-J. Reissman, I.E. Magnin and T. Katila. Model Extraction from Magnetic Resonance Volume Data Using the Deformable Pyramid. Medical Image Analysis, 3(4): 387-406, 1999.
  • J. Lötjönen, I.E. Magnin, L. Reinhardt, J. Nenonen and T. Katila. Automatic Reconstruction of 3D Geometry Using 2D Projections and a Geometric Prior Model. Lecture Notes in Computer Science 1679: Medical Image Computing and Computer-Assisted Intervention, MICCAI 99, C. Taylor, A. Colchester (Eds.), Springer, 192-201, 1999.
  • J. Lötjönen, I.E. Magnin, J. Nenonen and T. Katila. Reconstruction of 3D Geometry Using 2D Profiles and a Geometric Prior Model. IEEE Transactions on Medical Imaging, 18(10): 992-1002, 1999.
  • J. Lötjönen, P-J. Reissman, I.E. Magnin, J. Nenonen and T. Katila. A Triangulation Method of an Arbitrary Point Set for Biomagnetic Problems. IEEE Transactions on Magnetics, 34(4): 2228-2233, 1998.
  • R. Fenici, J. Nenonen, K. Pesola, P. Korhonen, J. Lötjönen, M. Mäkijärvi, L. Toivonen, V-P. Poutanen, P. Keto, T. Katila. Non-fluoroscopic localisation of an amagnetic stimulation catheter by multichannel magnetocardiography. Pacing Clin. Electrophysiol, 22: 1210-1220, 1999.
  • K. Pesola, J. Lötjönen, J. Nenonen, I.E. Magnin, K. Lauerma, R. Fenici and T. Katila. The effect of geometry and topology differences in boundary element models on magnetocardiographic localization accuracy. Accepted for publication in IEEE Transactions on Biomedical Engineering, 2000.
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