基于先验信息的肺部电阻抗成像算法

电阻抗层析成像 (EIT) 系统逆问题求解的欠定性是导致重构图像分辨率低的主要原因之一.结合先验信息,改善逆问题的不适定性,提高算法鲁棒性是提高成像质量可行的办法.本研究基于Tikhonov正则化算法,结合肺部组织结构、器官电导率分布参数以及肺部呼吸动态结构变化等先验信息,构建正则化矩阵,进行EIT图像重构.研究结果表明,结合先验信息的Tikhonov正则化图像重构算法减小了成像相对误差,改进了图像质量,EIT用于区域性肺部通气变化的检测和监护是可行的.

An Image Reconstruction Algorithm Based on Prior Information for the Pulmonary Electrical Impedance Tomography

In electrical impedance tomography (EIT), one of the main reasons for poor quality of reconstructed image is ill-posed property of the inverse problem. In order to improve image quality, one of the feasible methods is to incorporate prior information. Based on conventional Tikhonov regularization method to reconstruct EIT image, the regularization matrix, combined with prior information, was reconstructed. The prior information included the structure property of human thorax, conductivity distribution information of organs and dynamic information about lung's structure change during the respiration process. The research results indicated that the proposed method reduced the relative error of the reconstructed images, and improved the differentiability and quality of the reconstructed image. It is promising in detecting and mornitoring regional lung ventilation change by means of EIT.