Jain H, Isaacson D, Edic P M, Newell J C
Biomedical Engineering Department, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
IEEE Trans Biomed Eng. 1997 Nov;44(11):1051-60. doi: 10.1109/10.641332.
Electrical impedance tomography (EIT) uses low-frequency current and voltage measurements made on the boundary of a body to compute the conductivity distribution within the body. Since the permittivity distribution inside the body also contributes significantly to the measured voltages, the present reconstruction algorithm images complex conductivity distributions. A finite element model (FEM) is used to solve the forward problem, using a 6017-node mesh for a piecewise-linear potential distribution. The finite element solution using this mesh is compared with the analytical solution for a homogeneous field and a maximum error of 0.05% is observed in the voltage distribution. The boundary element method (BEM) is also used to generate the voltage data for inhomogeneous conductivity distributions inside regions with noncircular boundaries. An iterative reconstruction algorithm is described for approximating both the conductivity and permittivity distributions from this data. The results for an off-centered inhomogeneity showed a 35% improvement in contrast from that seen with only one iteration, for both the conductivity and the permittivity values. It is also shown that a significant improvement in images results from accurately modeling a noncircular boundary. Both static and difference images are distorted by assuming a circular boundary and the amount of distortion increases significantly as the boundary shape becomes more elliptical. For a homogeneous field in an elliptical body with axis ratio of 0.73, an image reconstructed assuming the boundary to be circular has an artifact at the center of the image with an error of 20%. This error increased to 37% when the axis ratio was 0.64. A reconstruction algorithm which used a mesh with the same axis ratio as the elliptical boundary reduced the error in the conductivity values to within 0.5% of the actual values.
电阻抗断层成像（EIT）利用在人体边界进行的低频电流和电压测量来计算人体内的电导率分布。由于人体内的介电常数分布对测量电压也有显著贡献，因此目前的重建算法能够对复杂的电导率分布进行成像。使用有限元模型（FEM）来解决正向问题，对于分段线性电位分布采用6017节点网格。将使用该网格的有限元解与均匀场的解析解进行比较，在电压分布中观察到的最大误差为0.05%。边界元法（BEM）也用于生成具有非圆形边界区域内非均匀电导率分布的电压数据。描述了一种迭代重建算法，用于从该数据中近似电导率和介电常数分布。对于偏心非均匀性的结果表明，对于电导率和介电常数的值，与仅进行一次迭代相比，对比度提高了35%。还表明，通过精确建模非圆形边界，图像有显著改善。假设为圆形边界会使静态图像和差分图像都产生失真，并且随着边界形状变得更椭圆，失真量会显著增加。对于轴比为0.73的椭圆形物体中的均匀场，假设边界为圆形重建的图像在图像中心有一个伪影，误差为20%。当轴比为0.64时，该误差增加到37%。一种使用与椭圆形边界具有相同轴比的网格的重建算法将电导率值的误差降低到实际值的0.5%以内。
