Department of Mathematics, Chungbuk National University, Cheongju 28644, Korea.
Department of Mathematics and School of Biomedical Engineering, Colorado State University, Fort Collins, CO 80523, USA.
Sensors (Basel). 2021 Aug 21;21(16):5635. doi: 10.3390/s21165635.
Bedside imaging of ventilation and perfusion is a leading application of 2-D medical electrical impedance tomography (EIT), in which dynamic cross-sectional images of the torso are created by numerically solving the inverse problem of computing the conductivity from voltage measurements arising on electrodes due to currents applied on electrodes on the surface. Methods of reconstruction may be direct or iterative. Calderón's method is a direct reconstruction method based on complex geometrical optics solutions to Laplace's equation capable of providing real-time reconstructions in a region of interest. In this paper, the importance of accurate modeling of the electrode location on the body is demonstrated on simulated and experimental data, and a method of including a priori spatial information in dynamic human subject data is presented. The results of accurate electrode modeling and a spatial prior are shown to improve detection of inhomogeneities not included in the prior and to improve the resolution of ventilation and perfusion images in a human subject.
床边通气和灌注成像（Bedside imaging of ventilation and perfusion）是二维医学电阻抗断层成像（2-D medical electrical impedance tomography，EIT）的主要应用之一，它通过数值求解计算由于电极表面施加电流而在电极上产生的电压测量值的电导率的逆问题，创建体部的动态横截面图像。重建方法可以是直接的或迭代的。Calderón 方法是一种基于拉普拉斯方程的复几何光学解的直接重建方法，能够在感兴趣区域提供实时重建。在本文中，通过模拟和实验数据证明了准确建模电极在人体上的位置的重要性，并提出了一种在动态人体数据中包含先验空间信息的方法。结果表明，准确的电极建模和空间先验有助于提高对先验中未包含的不均匀性的检测，并提高人体通气和灌注图像的分辨率。
