Gao Zengfeng, Darma Panji Nursetia, Kawashima Daisuke, Takei Masahiro
Division of Fundamental Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan.
J Electr Bioimpedance. 2023 Jan 8;13(1):106-115. doi: 10.2478/joeb-2022-0015. eCollection 2022 Jan.
The image reconstruction in electrical impedance tomography (EIT) has low accuracy due to the approximation error between the measured voltage change and the approximated voltage change, from which the object cannot be accurately reconstructed and quantitatively evaluated. A voltage approximation model based on object-oriented sensitivity matrix estimation (OO-SME model) is proposed to reconstruct the image with high accuracy. In the OO-SME model, a sensitivity matrix of the object-field is estimated, and the sensitivity matrix change from the background-field to the object-field is estimated to optimize the approximated voltage change, from which the approximation error is eliminated to improve the reconstruction accuracy. Against the existing linear and nonlinear models, the approximation error in the OO-SME model is eliminated, thus an image with higher accuracy is reconstructed. The simulation shows that the OO-SME model reconstructs a more accurate image than the existing models for quantitative evaluation. The relative accuracy (RA) of reconstructed conductivity is increased up to 83.98% on average. The experiment of lean meat mass evaluation shows that the RA of lean meat mass is increased from 7.70% with the linear model to 54.60% with the OO-SME model. It is concluded that the OO-SME model reconstructs a more accurate image to evaluate the object quantitatively than the existing models.
由于测量电压变化与近似电压变化之间存在近似误差，电阻抗断层成像（EIT）中的图像重建精度较低，导致无法对物体进行精确重建和定量评估。为了实现高精度图像重建，提出了一种基于面向对象灵敏度矩阵估计的电压近似模型（OO-SME模型）。在OO-SME模型中，估计目标场的灵敏度矩阵，并估计从背景场到目标场的灵敏度矩阵变化，以优化近似电压变化，从而消除近似误差，提高重建精度。与现有的线性和非线性模型相比，OO-SME模型消除了近似误差，从而重建出精度更高的图像。仿真结果表明，对于定量评估，OO-SME模型重建的图像比现有模型更准确。重建电导率的相对精度（RA）平均提高到83.98%。瘦肉量评估实验表明，瘦肉量的RA从线性模型的7.70%提高到OO-SME模型的54.60%。研究得出结论，与现有模型相比，OO-SME模型能够重建更精确的图像，以便对物体进行定量评估。
