Performance of a minimum-support imaging method for biomagnetic source localization

S. S. Nagarajan, O. Portniaguine, K. Sekihara, D. Hwang, C. Johnson

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

Images of brain activation typically comprise of small patches of activation with large regions of non-activation. Such images can be considered as minimum support images, where non-zero values occupy only a small area. In this paper, we propose a new minimum support imaging method that solves the bio-electromagnetic inverse problem. We formulate the problem using Lead-fields under Tikhonov theory which replaces the original ill-posed inverse problem by a well-posed minimization of a Tikhonov parametric functional consisting of a sum of the misfit and stabilizer functions. The stabilizer function embeds apriori assumptions about brain activation, which we assume is minimum-support. We illustrate the proposed method in simulations. Using realistic head and sensor geometry, we generate forward model MEG data for multiple-dipole sources. This simulated forward model data with additive noise was used to perform data inversion. Monte Carlo simulations indicate that the accuracy in localization of single dipoles is 2.1 mm for low noise conditions. While this performance is consistent with performance of conventional parametric dipole inversion, the minimum support method is capable of reconstructing multiple distributed sources.

Original languageEnglish
Pages (from-to)2006-2007
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume3
Publication statusPublished - 2002 Dec 1
EventProceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) - Houston, TX, United States
Duration: 2002 Oct 232002 Oct 26

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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