Forward problem solution of electromagnetic source imaging using a new BEM formulation with high-order elements.

Representations of the active cell populations on the cortical surface via electric and magnetic measurements are known as electromagnetic source images (EMSIs) of the human brain. Numerical solution of the potential and magnetic fields for a given electrical source distribution in the human brain is an essential part of electromagnetic source imaging. In this study, the performance of the boundary element method (BEM) is explored with different surface element types. A new BEM formulation is derived that makes use of isoparametric linear, quadratic or cubic elements. The surface integration is performed with Gauss quadrature. The potential fields are solved assuming a concentric three-shell model of the human head for a tangential dipole at different locations. In order to achieve 2% accuracy in potential solutions, the number of quadratic elements is of the order of hundreds. However, with linear elements, this number is of the order of ten thousand. The relative difference measures (RDMs) are obtained for the numerical models that use different element types. The numerical models that employ quadratic and cubic element types provide superior performance over linear elements in terms of accuracy in solutions. Assuming a homogeneous sphere model of the head, the RDMs are also obtained for the three components (radial and tangential) of the magnetic fields. The RDMs obtained for the tangential fields are, in general, much higher than those obtained for the radial fields. Both quadratic and cubic elements provide superior performance compared with linear elements for a wide range of dipole locations.

[1]  R E Ideker,et al.  Eccentric dipole in a spherical medium: generalized expression for surface potentials. , 1973, IEEE transactions on bio-medical engineering.

[3]  J.C. Mosher,et al.  Multiple dipole modeling and localization from spatio-temporal MEG data , 1992, IEEE Transactions on Biomedical Engineering.

[4]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[5]  J. P. Ary,et al.  Location of Sources of Evoked Scalp Potentials: Corrections for Skull and Scalp Thicknesses , 1981, IEEE Transactions on Biomedical Engineering.

[6]  B.N. Cuffin,et al.  EEG localization accuracy improvements using realistically shaped head models , 1996, IEEE Transactions on Biomedical Engineering.

[7]  M. Lynn,et al.  The application of electromagnetic theory to electrocardiology. I. Derivation of the integral equations. , 1967, Biophysical journal.

[8]  D. Geselowitz On bioelectric potentials in an inhomogeneous volume conductor. , 1967, Biophysical journal.

[9]  G. R. Cowper,et al.  Gaussian quadrature formulas for triangles , 1973 .

[10]  J. D. Munck,et al.  A fast method to compute the potential in the multisphere model (EEG application) , 1993, IEEE Transactions on Biomedical Engineering.

[11]  Lauri Parkkonen,et al.  A 122-channel whole-cortex SQUID system for measuring the brain's magnetic fields , 1993 .

[12]  D. Geselowitz,et al.  Model studies of the magnetocardiogram. , 1973, Biophysical journal.

[13]  R. Barr,et al.  Determining surface potentials from current dipoles, with application to electrocardiography. , 1966, IEEE transactions on bio-medical engineering.

[14]  D. A. Driscoll,et al.  EEG electrode sensitivity--an application of reciprocity. , 1969, IEEE transactions on bio-medical engineering.

[15]  Dietrich Lehmann,et al.  Evaluation of Methods for Three-Dimensional Localization of Electrical Sources in the Human Brain , 1978, IEEE Transactions on Biomedical Engineering.

[16]  Onno W. Weier,et al.  On the numerical accuracy of the boundary element method (EEG application) , 1989, IEEE Transactions on Biomedical Engineering.

[17]  Nevzat G. Gencer,et al.  A new boundary element method formulation for the forward problem solution of electro-magnetic source imaging , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[18]  J. Meijs,et al.  The EEG and MEG, Using a Model of Eccentric Spheres to Describe the Head , 1987, IEEE Transactions on Biomedical Engineering.

[19]  B. Neil Cuffin,et al.  Magnetic Fields of a Dipole in Special Volume Conductor Shapes , 1977, IEEE Transactions on Biomedical Engineering.

[20]  M. Lynn,et al.  The Use of Multiple Deflations in the Numerical Solution of Singular Systems of Equations, with Applications to Potential Theory , 1968 .

[21]  J.-Z. Wang,et al.  Minimum-norm least-squares estimation: magnetic source images for a spherical model head , 1993, IEEE Transactions on Biomedical Engineering.

[22]  L. Parkkonen,et al.  122-channel squid instrument for investigating the magnetic signals from the human brain , 1993 .

[23]  A. Dale,et al.  Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.

[24]  Jia-Zhu Wang,et al.  MNLS inverse discriminates between neuronal activity on opposite walls of a simulated sulcus of the brain. , 1994, IEEE transactions on bio-medical engineering.

[25]  A. S. Ferguson,et al.  Factors affecting the accuracy of the boundary element method in the forward problem. I. Calculating surface potentials , 1997, IEEE Transactions on Biomedical Engineering.

[26]  D. Geselowitz On the magnetic field generated outside an inhomogeneous volume conductor by internal current sources , 1970 .

[27]  H. Spekreijse,et al.  Mathematical dipoles are adequate to describe realistic generators of human brain activity , 1988, IEEE Transactions on Biomedical Engineering.

[28]  M. Hämäläinen,et al.  Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data , 1989, IEEE Transactions on Biomedical Engineering.