中文摘要：

提出了融合ECT测量信息和被测对象动态演化信息的新型图像重建模型;基于Tikhonov正则化方法,建立一个同时考虑了ECT测量信息、被测对象动态演化信息、时间与空间约束的新型图像重建目标泛涵,将图像重建问题转化为最优化问题;提出了集成分裂Bregman迭代法优势的新型算法求解该目标泛涵。数值仿真结果表明,所提出的图像重建算法其图像重建质量均优于OIOR算法、STR算法及PLI算法;同时由于所提出的图像重建算法同时考虑了测量数据和重建模型的不精确性,其抵抗测量噪声的能力得以提高。

英文摘要：

A new dynamic image reconstruction model is proposed,which integrates the ECT measurement information and the dynamic evolution information of the measured imaging object. Within the framework of the Tikhonov regularization method,a new image reconstruction objective functional is established,which simultaneously considers the ECT measurement information,the dynamic evolution information of the measured time-varying object and the temporal and spatial constraints,and converts the ECT image reconstruction problem into an optimization problem. A new iteration algorithm that incorporates the advantage of the split Bregman iteration method is proposed to obtain the optimal solution of the objective functional. Numerical simulation results indicate that the quality of the reconstructed image for the proposed image reconstruction method is better than those for other image reconstruction algorithms,such as OIOR,STR and PLI algorithms. Besides,the capacity of resisting measurement noise for the proposed image reconstruction method is improved because the uncertainties of the measurement data and reconstruction model are considered in the algorithm.