Shen Hui-min, Lee Kok-Meng, Hu Liang, Foong Shaohui, Fu Xin
State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou, China.
Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Med Biol Eng Comput. 2016 Jan;54(1):177-89. doi: 10.1007/s11517-015-1381-9. Epub 2015 Sep 11.
Localization of active neural source (ANS) from measurements on head surface is vital in magnetoencephalography. As neuron-generated magnetic fields are extremely weak, significant uncertainties caused by stochastic measurement interference complicate its localization. This paper presents a novel computational method based on reconstructed magnetic field from sparse noisy measurements for enhanced ANS localization by suppressing effects of unrelated noise. In this approach, the magnetic flux density (MFD) in the nearby current-free space outside the head is reconstructed from measurements through formulating the infinite series solution of the Laplace's equation, where boundary condition (BC) integrals over the entire measurements provide "smooth" reconstructed MFD with the decrease in unrelated noise. Using a gradient-based method, reconstructed MFDs with good fidelity are selected for enhanced ANS localization. The reconstruction model, spatial interpolation of BC, parametric equivalent current dipole-based inverse estimation algorithm using reconstruction, and gradient-based selection are detailed and validated. The influences of various source depths and measurement signal-to-noise ratio levels on the estimated ANS location are analyzed numerically and compared with a traditional method (where measurements are directly used), and it was demonstrated that gradient-selected high-fidelity reconstructed data can effectively improve the accuracy of ANS localization.
在脑磁图中，从头部表面测量结果定位活跃神经源（ANS）至关重要。由于神经元产生的磁场极其微弱，随机测量干扰导致的显著不确定性使其定位变得复杂。本文提出了一种基于稀疏噪声测量重建磁场的新型计算方法，通过抑制无关噪声的影响来增强ANS定位。在这种方法中，通过求解拉普拉斯方程的无穷级数解，从测量结果重建头部外部附近无电流空间中的磁通密度（MFD），其中在整个测量上的边界条件（BC）积分随着无关噪声的减少提供“平滑”的重建MFD。使用基于梯度的方法，选择具有高保真度的重建MFD以增强ANS定位。详细介绍并验证了重建模型、BC的空间插值、基于参数等效电流偶极子的使用重建的逆估计算法以及基于梯度的选择。数值分析了各种源深度和测量信噪比水平对估计的ANS位置的影响，并与传统方法（直接使用测量结果）进行比较，结果表明梯度选择的高保真重建数据可以有效提高ANS定位的准确性。
