Caeiros Jorge, Martins Raul C, Gil Bruno
Telecommunications Institute, Lisbon, Portugal.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:6239-42. doi: 10.1109/EMBC.2012.6347420.
A linear image reconstruction algorithm for solving the Magnetic Induction Tomography inverse problem is presented. It's an optimization process to determine a reconstruction matrix that does the best mapping between a set of training parameter vectors and their respective measurements dictated by a forward model. It allows the simultaneous 3D reconstructions of the electric conductivity, electric permittivity and magnetic permeability. The results were compared with the ones obtained from a single-step regularized Gauss-Newton method and a reduction of 15% in the image error was verified. The behavior of the developed algorithm in a simulated clinical environment was also assessed using a realistic bio-impedance model of the human head, derived from a high resolution magnetic resonance image.
提出了一种用于求解磁感应断层成像逆问题的线性图像重建算法。这是一个优化过程，用于确定一个重建矩阵，该矩阵能在一组训练参数向量与其由正向模型决定的各自测量值之间实现最佳映射。它允许同时对电导率、电容率和磁导率进行三维重建。将结果与通过单步正则化高斯 - 牛顿法获得的结果进行了比较，验证了图像误差降低了15%。还使用从高分辨率磁共振图像导出的逼真人体头部生物阻抗模型，评估了所开发算法在模拟临床环境中的性能。
