Chen Yuyan, Wang Xu, Yang Dan, Lu Yi
College of Information Science and Engineering, Northeastern University, Shenyang 110004, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2013 Feb;30(1):162-5.
Magnetic induction tomography (MIT) image reconstruction is a typical ill-posed problem, and its numerical solution is unstable. A new image reconstruction algorithm based on the L(P)-norm, which solves the ill-posed inverse problem of MIT and improves the quality of reconstructed image, is presented in this paper. The new algorithm not only overcomes the problem of numerical instability of the MIT image reconstruction, but also improves the quality of the reconstructed image and enhances the spatial resolution of the reconstructed image. Simulation results showed that the quality of the reconstructed image obtained using the presented algorithm was better than that using Tikhonov regularization algorithm and that using the variation regularization algorithm, so it could be an effective method for the MIT.
磁感应断层成像（MIT）图像重建是一个典型的不适定问题，其数值解不稳定。本文提出了一种基于L(P)范数的新图像重建算法，该算法解决了MIT的不适定逆问题，提高了重建图像的质量。新算法不仅克服了MIT图像重建数值不稳定的问题，还提高了重建图像的质量，增强了重建图像的空间分辨率。仿真结果表明，使用该算法获得的重建图像质量优于使用Tikhonov正则化算法和变分正则化算法获得的重建图像质量，因此它可能是一种适用于MIT的有效方法。
