Vauhkonen M, Karjalainen P A, Kaipio J P
Department of Applied Physics, University of Kuopio, Finland.
IEEE Trans Biomed Eng. 1998 Apr;45(4):486-93. doi: 10.1109/10.664204.
In electrical impedance tomography (EIT), an estimate for the cross-sectional impedance distribution is obtained from the body by using current and voltage measurements made from the boundary. All well-known reconstruction algorithms use a full set of independent current patterns for each reconstruction. In some applications, the impedance changes may be so fast that information on the time evolution of the impedance distribution is either lost or severely blurred. In this paper, we propose an algorithm for EIT reconstruction that is able to track fast changes in the impedance distribution. The method is based on the formulation of EIT as a state-estimation problem and the recursive estimation of the state with the aid of the Kalman filter. The performance of the proposed method is evaluated with a simulation of human thorax in a situation in which the impedances of the ventricles change rapidly. We show that with optimal current patterns and proper parameterization, the proposed approach yields significant enhancement of the temporal resolution over the conventional reconstruction strategy.
在电阻抗断层成像（EIT）中，通过使用从边界进行的电流和电压测量，从人体获得横截面阻抗分布的估计值。所有知名的重建算法在每次重建时都使用一整套独立的电流模式。在某些应用中，阻抗变化可能非常快，以至于阻抗分布随时间演变的信息要么丢失，要么严重模糊。在本文中，我们提出了一种用于EIT重建的算法，该算法能够跟踪阻抗分布的快速变化。该方法基于将EIT表述为状态估计问题，并借助卡尔曼滤波器对状态进行递归估计。在所提出的方法的性能通过在心室阻抗快速变化的情况下对人体胸部的模拟进行评估。我们表明，通过最优电流模式和适当的参数化，所提出的方法比传统重建策略在时间分辨率上有显著提高。
