A New Iterative Algorithm Based on Correction of Sensitivity Matrix for Electrical Resistance Tomography

Chen, Yutong; Han, Yan; Yang, Wuqiang; Li, Kun

doi:10.1155/2019/6384132

Cited by 44 publications

(4 citation statements)

References 15 publications

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“…Tobias et al (2019) proposed a frequency difference electrical impedance tomography algorithm based on the absolute value, which has strong robustness under noise interference. A new iterative algorithm based on the sensitivity matrix is proposed by Chen et al (2019).…”

Section: Inverse Problemmentioning

confidence: 99%

The Research Progress of Electrical Impedance Tomography for Lung Monitoring

Shi

Yang

Xie

et al. 2021

Front. Bioeng. Biotechnol.

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Medical imaging can intuitively show people the internal structure, morphological information, and organ functions of the organism, which is one of the most important inspection methods in clinical medical diagnosis. Currently used medical imaging methods can only be applied to some diagnostic occasions after qualitative lesions have been generated, and the general imaging technology is usually accompanied by radiation and other conditions. However, electrical impedance tomography has the advantages of being noninvasive and non-radiative. EIT (Electrical Impedance Tomography) is also widely used in the early diagnosis and treatment of some diseases because of these advantages. At present, EIT is relatively mature and more and more image reconstruction algorithms are used to improve imaging resolution. Hardware technology is also developing rapidly, and the accuracy of data collection and processing is continuously improving. In terms of clinical application, EIT has also been used for pathological treatment of lungs, the brain, and the bladder. In the future, EIT has a good application prospect in the medical field, which can meet the needs of real-time, long-term monitoring and early diagnosis. Aiming at the application of EIT in the treatment of lung pathology, this article reviews the research progress of EIT, image reconstruction algorithms, hardware system design, and clinical applications used in the treatment of lung diseases. Through the research and introduction of several core components of EIT technology, it clarifies the characteristics of EIT system complexity and its solutions, provides research ideas for subsequent research, and once again verifies the broad development prospects of EIT technology in the future.

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Section: Inverse Problemmentioning

confidence: 99%

The Research Progress of Electrical Impedance Tomography for Lung Monitoring

Shi

Yang

Xie

et al. 2021

Front. Bioeng. Biotechnol.

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“…Chen et al. enhanced the quality of the image by iteratively correcting the sensitivity matrix deviation, albeit at the expense of heightened computational demands [ 32 ]. The integration of two prior pieces of information for sensitivity matrix generation yields a well-balanced and robust performance, but the observed improvement in reconstruction results is not substantial [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

“…Correcting the sensitivity matrix using existing information has proven effective in enhancing reconstruction accuracy, as demonstrated by the use of a redundant sensitivity matrix for improved results [31]. Chen et al enhanced the quality of the image by iteratively correcting the sensitivity matrix deviation, albeit at the expense of heightened computational demands [32]. The integration of two prior pieces of information for sensitivity matrix generation yields a well-balanced and robust performance, but the observed improvement in reconstruction results is not substantial [33].…”

Section: Introductionmentioning

confidence: 99%

Posterior Approximate Clustering-Based Sensitivity Matrix Decomposition for Electrical Impedance Tomography

Wang,

Sun,

2024

Sensors

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This paper introduces a sensitivity matrix decomposition regularization (SMDR) method for electric impedance tomography (EIT). Using k-means clustering, the EIT-reconstructed image can be divided into four clusters, derived based on image features, representing posterior information. The sensitivity matrix is then decomposed into distinct work areas based on these clusters. The elimination of smooth edge effects is achieved through differentiation of the images from the decomposed sensitivity matrix and further post-processing reliant on image features. The algorithm ensures low computational complexity and avoids introducing extra parameters. Numerical simulations and experimental data verification highlight the effectiveness of SMDR. The proposed SMDR algorithm demonstrates higher accuracy and robustness compared to the typical Tikhonov regularization and the iterative penalty term-based regularization method (with an improvement of up to 0.1156 in correlation coefficient). Moreover, SMDR achieves a harmonious balance between image fidelity and sparsity, effectively addressing practical application requirements.

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“…In the article titled "A New Iterative Algorithm Based on Correction of Sensitivity Matrix for Electrical Resistance Tomography" [1], there was an error in the "Introduction" section, where "ERT has been widely used for chemical […”

mentioning

confidence: 99%