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Proceedings Paper

Signal processing for the biomagnetic reconstruction of time-variant sources
Author(s): Klaus Abraham-Fuchs; W. Harer; Sebastian M. Schneider; Helmut Reichenberger; Arnulf Oppelt
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Paper Abstract

Biomagnetism is a promising new tool for the noninvasive 3D -localization of electrophysiological activity. For reconstructing current distributions from biomagnetic fields the quasi-stationary limit of the theory of electromagnetic fields can be used [1]. This means that, once one has recorded a time series of magnetic field data at multiple sensor positions, one can pick out the magnetic field distribution at any arbitrary time instant. The momentary source current distribution can then be reconstructed from this field regardless of what happens with the sources before or after this time instant. Though this is an important feature in the evaluation of biomagnetic data, taking into account apriori- and physiological knowledge can improve the biomagnetic localization result significantly. The sources to be reconstructed are electrophysiological processes, and the time course and propagation of these activities obviously depends in a causal way on their previous history. So in fact we have much more information than the quasi-static magnetic field map alone, an information which can be used to improve the reliability of the reconstruction result. Biomagnetic signal variation in time and space can be used for the improvement of data evaluation algorithms and in simulation studies for biomagnetic system optimization. In this paper we will focus on four fields, which will be discussed in detail in the following sections: - use of source model simulations as function of time to optimize frequency passband and data acquisition rate with respect to source reconstruction accuracy - half-automated validation of the reconstruction result by comparison of the time course of the equivalent source with physiological criteria - separation of an electric activity of interest from simultaneous processes - sensitive pattern recognition algorithm to recognize and average similar events

Paper Details

Date Published: 1 November 1990
PDF: 11 pages
Proc. SPIE 1351, Digital Image Synthesis and Inverse Optics, (1 November 1990); doi: 10.1117/12.23655
Show Author Affiliations
Klaus Abraham-Fuchs, Siemens AG (Germany)
W. Harer, Siemens AG (Germany)
Sebastian M. Schneider, Siemens AG (Germany)
Helmut Reichenberger, Siemens AG (Germany)
Arnulf Oppelt, Siemens AG (Germany)

Published in SPIE Proceedings Vol. 1351:
Digital Image Synthesis and Inverse Optics
Arthur F. Gmitro; Paul S. Idell; Ivan J. LaHaie, Editor(s)

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