Dai Tao, Gómez-Laberge Camille, Adler Andy
Systems and Computer Engineering, Carleton University, Ottawa, Canada.
Physiol Meas. 2008 Jun;29(6):S77-88. doi: 10.1088/0967-3334/29/6/S07. Epub 2008 Jun 10.
Electrical impedance tomography (EIT) reconstructs a conductivity change image within a body from electrical measurements on the body surface; while it has relatively low spatial resolution, it has a high temporal resolution. One key difficulty with EIT measurements is due to the movement and position uncertainty of the electrodes, especially due to breathing and posture change. In this paper, we develop an approach to reconstruct both the conductivity change image and the electrode movements from the temporal sequence of EIT measurements. Since both the conductivity change and electrode movement are slow with respect to the data frame rate, there are significant temporal correlations which we formulate as priors for the regularized image reconstruction model. Image reconstruction is posed in terms of a regularization matrix and a Jacobian matrix which are augmented for the conductivity change and electrode movement, and then further augmented to concatenate the d previous and future frames. Results are shown for simulation, phantom and human data, and show that the proposed algorithm yields improved resolution and noise performance in comparison to a conventional one-step reconstruction method.
电阻抗断层成像（EIT）通过对体表进行电测量来重建体内的电导率变化图像；虽然其空间分辨率相对较低，但具有较高的时间分辨率。EIT测量的一个关键难题在于电极的移动和位置不确定性，尤其是由于呼吸和姿势变化导致的。在本文中，我们开发了一种方法，可从EIT测量的时间序列中重建电导率变化图像和电极移动情况。由于电导率变化和电极移动相对于数据帧率都较为缓慢，存在显著的时间相关性，我们将其作为正则化图像重建模型的先验条件。图像重建是根据正则化矩阵和雅可比矩阵进行的，这些矩阵针对电导率变化和电极移动进行了扩充，然后进一步扩充以连接d个先前和未来的帧。给出了模拟、体模和人体数据的结果，结果表明，与传统的一步重建方法相比，所提出的算法在分辨率和噪声性能方面都有所提高。
