头部组织三维核磁共振电阻抗成像算法的仿真研究

A RECONSTRUCTION ALGORITHM FOR HEAD 3D MAGNETIC RESONANCE ELECTRICAL IMPEDANCE TOMOGRAPHY:SIMULATION STUDY

文章给出了一种基于核磁共振技术的三维阻抗成像(电导率分布)重构算法,并将该方法应用于人体头部组织电导率分布重构上。该代数重构方法是利用高分辨率的核磁共振成像系统对成像物体进行三维构建和不同组织的边界区分,根据核磁共振系统中测量得到的磁感应强度Bz和By分量并结合有限元数值计算得到的电流密度分布J组成非线性矩阵,通过迭代求解此非线性矩阵,来解决三维电导率分布的重构问题。在三层球头模型(包括头皮、颅骨和大脑)上分别进行的仿真实验结果表明,该算法具有较强的抗噪声能力和较好的收敛性,重构的头部电导率分布图像具有较高的精确性。

In the paper, an algebraic reconstruction algorithm based on magnetic resonance electrical impedance tomography was presented for 3-dimensional （3D） electrical impedance tomography, especially to image the 3D continuous impedance distribution of the head tissues. In this method, the MRI system with high resolution was used to set up the 3D model of the object and to identify the boundary of different tissues. Then a non-linear matrix was composed of the measured magnetic flux density Bx and By combined with the current density J gained from the numerical computation by the finite element method. The solution of the non-linear matrix, which was solved iteratively, wass the reconstruction electrical impedance tomography. Numerical simulations were performed on a concentric three-sphere head model （scalp-skull-brain model）. The results show that the 3D continuous conductivity reconstruction method has higher accuracy, fast convergence ability and better robustness against noise.