%0 English Abstract
%T [Research of electrical impedance tomography based on multilayer artificial neural network optimized by Hadamard product for human-chest models].
%A Song Z
%A Li J
%A Wen J
%A Wan N
%A Ma J
%A Zhang Y
%A Hu Y
%A Gao Z
%J Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
%V 41
%N 3
%D 2024 Jun 25
%M 38932528
暂无%R 10.7507/1001-5515.202305047
%X Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in human-chest models, the HMANN algorithm was proposed using the Hadamard product to optimize multilayer artificial neural networks (MANN). The reconstructed images of the HMANN algorithm were compared with those of the generalized vector sampled pattern matching (GVSPM) algorithm, truncated singular value decomposition (TSVD) algorithm, backpropagation (BP) neural network algorithm, and traditional MANN algorithm. The simulation results showed that the correlation coefficient of the reconstructed images obtained by the HMANN algorithm was increased by 17.30% in the circular cross-section models compared with the MANN algorithm. It was increased by 13.98% in the lung cross-section models. In the lung cross-section models, some of the correlation coefficients obtained by the HMANN algorithm would decrease. Nevertheless, the HMANN algorithm retained the image information of the MANN algorithm in all models, and the HMANN algorithm had fewer artifacts in the reconstructed images. The distinguishability between the objects and the background was better compared with the traditional MANN algorithm. The algorithm could improve the correlation coefficient of the reconstructed images, and effectively remove the artifacts, which provides a new direction to effectively improve the quality of the reconstructed images for EIT.<BR>电阻抗成像（EIT）是一种无辐射、非侵入式的可视化诊断技术。为提高胸部电阻抗成像技术重建算法的成像分辨率和去伪影能力，本研究提出了一种利用Hadamard product优化多层神经网络（MANN）的HMANN算法。将HMANN算法的重建图像与广义矢量模式匹配（GVSPM）算法、截断奇异值分解（TSVD）算法、反向传播（BP）神经网络算法和传统MANN算法的重建图像进行对比，仿真结果表明：相对于MANN算法，HMANN算法重建图像的相关系数在圆截面模型中可以提高17.30%，在肺截面模型中可以提高13.98%。虽然肺截面模型中HMANN算法重建图像的部分相关系数会有所下降，但在所有模型中，HMANN算法保留了MANN算法的图像信息，同时HMANN算法重建图像的伪影更少，检测目标与背景的可识别度比传统MANN算法高。本研究可以提升重建图像的相关系数，有效去除重建图像的伪影，为EIT成像技术提供了一种有效提升重建图像质量的新思路。.