发表机构
School of Mathematics and Statistics, Shandong Normal University（山东师范大学数学与统计学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对EIT成像中相邻目标因空间灵敏度不足而合并的问题，提出利用初始重建图像构建高斯相似矩阵和局部结构掩模作为自生成先验，在后续重建中保持目标空间可分离性，数值与水槽实验验证了其有效性。
AI 中文摘要
电阻抗断层成像（EIT）是一种不适定的逆成像技术，其分辨率有限且空间上不均匀。位于空间灵敏度退化区域的电导率目标可能被重建为单个连通区域，导致相邻结构之间的空间可分离性丧失。在本工作中，我们提出了一种自生成的局部结构先验，用于抑制EIT重建中的目标合并。首先，利用现有的EIT重建方法从测量到的边界数据中获得初始电导率图像。随后，该重建图像被用作参考图像，根据图像像素之间的强度相似性构建高斯相似矩阵。然后，采用自适应选择的协方差矩阵特征向量来提取参考图像的主要结构信息。构建一个局部结构掩模，并将其纳入后续的EIT重建中，以保持相邻目标的空间可分离性。数值模拟和水槽实验结果表明，所提出的方法能够有效分离被传统EIT重建方法合并的相邻电导率目标。
英文摘要
Electrical impedance tomography (EIT) is an ill-posed inverse imaging technique with limited and spatially nonuniform resolution. Conductivity targets located in regions with degraded spatial sensitivity may be reconstructed as a single connected region, resulting in the loss of spatial separability between neighboring structures. In this work, we propose a self-generated local structural prior for suppressing target merging in EIT reconstruction. First, an initial conductivity image is obtained from the measured boundary data using an existing EIT reconstruction method. This reconstruction is subsequently used as a reference image to construct a Gaussian similarity matrix according to the intensity similarity between image pixels. The adaptively chosen eigenvector of the covariance matrix is then employed to extract the principal structural information of the reference image. A localized structural mask is constructed and incorporated into a subsequent EIT reconstruction to preserve the spatial separability of neighboring targets. Numerical and water tank experimental results demonstrate that the proposed method effectively separates neighboring conductivity targets that are merged by conventional EIT reconstruction methods.
