Magnetic Field Tomography: Theoretical Basis, Performance Measures and Limitations View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2000

AUTHORS

A. A. Ioannides , J. P. R. Bolton , M. J. Liu , P. D. Bamidis , L. C. Liu , J. Dammers , H. W. Müller-Gärtner

ABSTRACT

A general method for obtaining continuous solutions to the biomagnetic inverse problem was first presented during the 1989 Biomagnetism conference. From the outset, the method was developed for three-dimensional source spaces [1, 2] but, partly to reduce computational demands and partly for ease of presentation, the first major publication of the method [3] used two-dimensional reconstructions. Soon afterwards the algorithms were ported to a transputer, making possible the analysis of large sets of MEG data. The output (estimates of the primary current density in a three-dimensional source space) was displayed by taking slices (tomes) through the source space leading to a series of MFT (Magnetic Field Tomographie) images. Later forms of representation included time integrals and activation curves, describing the evolution of activity within specified Regions of Interest (ROI). Animations run on the transputer array and recorded on video, were by far the most effective presentation tool, showing in colour-coded form the changes in the activity against a background of coregistered MRI slices[4]. Early reconstructions used averaged data but the emphasis has recently shifted to analysis of single trial or continuous data [5, 6]. Each three-dimensional study was underpinned by thorough tests with Computer generated data which, however, have been only superficially reported because of space limitations. We have previously described our philosophy and method [3], the transputer based implementation [7] and the logic in the various algorithmic steps [8]. However, the lack of published material on the background tests must be the cause of misrepresentations of MFT in some recent publications dealing with distributed source analysis, e.g. [9]. The present paper makes good this omission and uses computer-generated data to illustrate the power and limitations of MFT as a 3D imaging method, using realistic sensor configurations and signals that are neurophysiologically realistic in terms of location and temporal characteristics. More... »

PAGES

257-260

Book

TITLE

Biomag 96

ISBN

978-1-4612-7066-9
978-1-4612-1260-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4612-1260-7_62

DOI

http://dx.doi.org/10.1007/978-1-4612-1260-7_62

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040647013


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