Impact of ISTA and FISTA iterative optimization algorithms on electrical impedance tomography image reconstruction.

Q3 Biochemistry, Genetics and Molecular Biology
Journal of Electrical Bioimpedance Pub Date : 2025-03-11 eCollection Date: 2025-01-01 DOI:10.2478/joeb-2025-0003
Quoc Tuan Nguyen Diep, Hoang Nhut Huynh, Thanh Ven Huynh, Minh Quan Cao Dinh, Anh Tu Tran, Trung Nghia Tran
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引用次数: 0

Abstract

Electrical Impedance Tomography (EIT) is a non-invasive method for imaging conductivity distributions within a target area. The inverse problem associated with EIT is nonlinear and ill-posed, leading to low spatial resolution reconstructions. Iterative algorithms are widely employed to address complex inverse problems. However, current iterative methods have notable limitations, such as the arbitrary and subjective selection of initial parameters, lengthy computational times due to numerous iterations, and the generation of reconstructions that suffer from shape blurring and a lack of higher-order detail. To address these issues, this study investigates the impact of using ISTA and FISTA iterative algorithms on the image reconstruction process in EIT. It focuses on enhancing the convergence and accuracy of EIT image reconstruction by evaluating the effectiveness of these optimization algorithms when applied to regularized inverse problems, using standard regularization techniques. ISTA and FISTA were compared to the NOSER and Newton-Raphson methods and validated through simulation and experimental results. The results show that ISTA and FISTA achieve better visualization and faster convergence than conventional methods in terms of computational efficiency when solving regularized problems, achieving conductivity reconstructions with an accuracy of above 80%. The paper concludes that while ISTA and FISTA significantly enhance EIT image reconstruction performance, the quality of the reconstructed images heavily depends on the choice of regularization methods and parameters, which are crucial to the reconstruction process.

ISTA和FISTA迭代优化算法对电阻抗断层成像重建的影响。
电阻抗断层成像(EIT)是一种非侵入性的成像目标区域内电导率分布的方法。与EIT相关的反问题是非线性和病态的,导致低空间分辨率的重建。迭代算法被广泛应用于求解复杂的逆问题。然而,目前的迭代方法有明显的局限性,例如初始参数的任意和主观选择,由于多次迭代而导致的计算时间长,以及生成的重建受到形状模糊和缺乏高阶细节的影响。为了解决这些问题,本研究探讨了使用ISTA和FISTA迭代算法对EIT图像重建过程的影响。通过评估这些优化算法应用于正则化逆问题时的有效性,利用标准正则化技术,重点提高了EIT图像重建的收敛性和准确性。将ISTA和FISTA与NOSER和Newton-Raphson方法进行了比较,并通过仿真和实验结果进行了验证。结果表明,在求解正则化问题时,ISTA和FISTA在计算效率方面比传统方法具有更好的可视化效果和更快的收敛速度,实现了电导率重建,准确率在80%以上。本文得出结论,尽管ISTA和FISTA显著提高了EIT图像的重建性能,但重建图像的质量很大程度上取决于正则化方法和参数的选择,而正则化方法和参数对重建过程至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Electrical Bioimpedance
Journal of Electrical Bioimpedance Engineering-Biomedical Engineering
CiteScore
3.00
自引率
0.00%
发文量
8
审稿时长
17 weeks
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