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An explicit method for the magnetoencephalography inverse problem based on data continuation

机译:基于数据连续性的脑磁图逆问题的显式方法

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We have proposed a direct method for magnetoencephalography inverse problem in which the number, positions, and moments of the equivalent current dipoles (ECDs) are reconstructed algebraically. Although it can provide a good initial solution for the optimization-based iterative algorithms, the problem is that it requires the data on a closed surface which encloses the source. In this paper, we use the data continuation method proposed by Popov where the magnetic field at the missing sensor positions is extrapolated from the measured MEG data by solving linear boundary integral equations. After the data on a closed surface is obtained, ECDs are reconstructed using the direct method. The proposed method is verified with numerical simulations and PHANTOM experiments.
机译:我们已经提出了一种解决脑磁图反问题的直接方法,其中等效电流偶极子(ECD)的数量,位置和力矩通过代数方式重构。尽管它可以为基于优化的迭代算法提供良好的初始解决方案,但问题是它需要数据在封闭源的封闭表面上。在本文中,我们使用Popov提出的数据连续方法,其中通过求解线性边界积分方程,从测得的MEG数据中推断出传感器缺失位置处的磁场。获得封闭表面上的数据后,使用直接方法重建ECD。数值仿真和PHANTOM实验验证了该方法的有效性。

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