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An Image Reconstruction Algorithm for a 12-Electrode Capacitively Coupled Electrical Resistance Tomography System Under 2-Electrode Excitation Strategy
IEEE Transactions on Instrumentation and Measurement ( IF 5.6 ) Pub Date : 2021-07-19 , DOI: 10.1109/tim.2021.3098388
Zhen Xu , Junchao Huang , Yandan Jiang , Baoliang Wang , Zhiyao Huang , Manuchehr Soleimani

An image reconstruction algorithm, which is developed for a 12-electrode capacitively coupled electrical resistance tomography (CCERT) system under 2-electrode excitation strategy, is proposed. Based on L-curve and Reginska’s method, truncated singular value decomposition (TSVD) is used to reconstruct the initial image. The algebraic reconstruction technique (ART) algorithm is used to obtain the final reconstructed image. Image reconstruction experiments are conducted by a 12-electrode CCERT system. The proposed algorithm (TSVD + ART) is compared with conventional linear back projection (LBP), Tikhonov, Landweber, ART, simultaneous iterative reconstruction technique (SIRT), total variation (TV), conjugate gradient (CG), and TSVD to evaluate its image reconstruction performance. Image reconstruction results show the proposed algorithm (TSVD + ART) can effectively exploit the advantages of 2-electrode excitation strategy and hence realize higher quality image reconstruction. Under 2-electrode excitation strategy, the proposed algorithm has an obvious advantage over conventional image reconstruction algorithms. Under 1-electrode excitation strategy, the image reconstruction performance is comparable or slightly improved compared with that of conventional image reconstruction algorithms. Image reconstruction results also indicate the TSVD is effective to obtain the initial reconstructed image. The quality of the initial reconstructed image can be significantly improved compared with that of classic LBP, either under 2-electrode excitation strategy or 1-electrode excitation strategy.

中文翻译:

2电极激励策略下12电极电容耦合电阻层析成像系统的图像重建算法

提出了一种基于2电极激励策略的12电极电容耦合电阻断层扫描(CCERT)系统的图像重建算法。基于L曲线和Reginska的方法,使用截断奇异值分解(TSVD)来重建初始图像。代数重建技术(ART)算法用于获得最终的重建图像。图像重建实验由12电极CCERT系统进行,所提算法(TSVD+ART)与传统线性反投影(LBP)、Tikhonov、Landweber、ART、同步迭代重建技术(SIRT)、全变差(TV )、共轭梯度 (CG) 和 TSVD 来评估其图像重建性能。图像重建结果表明,所提出的算法(TSVD+ART)可以有效地发挥2电极激励策略的优势,从而实现更高质量的图像重建。在2电极激励策略下,所提出的算法与传统的图像重建算法相比具有明显的优势。在单电极激励策略下,图像重建性能与传统图像重建算法相当或略有提高。图像重建结果也表明 TSVD 对获得初始重建图像是有效的。与经典 LBP 相比,无论是在 2 电极激励策略还是 1 电极激励策略下,初始重建图像的质量都可以显着提高。
更新日期:2021-08-03
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