A Reconstruction Method for Electrical Impedance Tomography Using Particle Swarm Optimization

Chen, Minyou; Hu, Gang; He, Wei; Yang, Yuqiang; Zhai, Jia

doi:10.1007/978-3-642-15597-0_38

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…The posterior density is the comprehensive probabilistic model of the EIT problem and characterises the vagueness in the unknown variables for available measurements. AI-based techniques such as Neural Networks [11]and evolutionary methods [12] were also employed for the EIT inverse problem. These methods are self-sufficient to determine the solution steps involved in solving the inverse EIT problem.…”

Section: Mathematical Modelling Of Inverse Problemmentioning

confidence: 99%

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Electrical Impedance Tomography – Image Reconstruction using Population-based Optimisation Algorithms

Khan¹,

Ling

Rizvi

2022

Preprint

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Preventing living tissues' direct exposure to ionising radiation has resulted in tremendous growth in medical imaging and e-health, enhancing intensive care of perilous patients and helping to improve quality of life. Moreover, the practice of image-reconstruction instruments that utilise ionising radiation significantly impacts the patient's health. Prolonged or frequent exposure to ionising radiation is linked to several illnesses like cancer. These factors urged the advancement of non-invasive approaches, for instance, Electrical Impedance Tomography (EIT), a portable, non-invasive, low-cost, and safe imaging method. EIT image reconstruction still demands more exploitation, as it is an inverse and ill-conditioned problem. Numerous numerical techniques are used to answer this problem without producing anatomically unpredictable outcomes. Evolutionary Computational techniques can be used as substitutes for the conventional methods that usually create low-resolution blurry images. EIT reconstruction techniques optimise the relative error of reconstruction using population-based optimisation methods presented in this work. Three advanced optimisation methods have been developed to facilitate the iterative procedure for avoiding anatomically erratic solutions. Three different optimising techniques, namely, a) Advanced Particle Swarm Optimisation Algorithm (APSO), b) Advanced Gravitational Search Algorithm (AGSA), and c) Hybrid Gravitational Search Particle Swarm Optimization Algorithm (HGSPSO), are used. By utilising the advantages of these proposed techniques, the performance in terms of convergence and solution stability is improved. EIT images were obtained from the EIDORS library database for two case studies. The image reconstruction was optimised using the three proposed algorithms. EIDORS library was used for generating and solving forward and reverse problems. Two case studies were undertaken, i.e. circular tank simulation and gastric emptying. The results thus obtained are analysed and presented as a real-world application of population-based optimisation methods. Results obtained from the proposed methods are quantitatively assessed with ground truth images using the relative mean squared error, confirming that a low error value is reached in the results. HGSPSO algorithm has superior performance compared to the other proposed methods in terms of solution quality and stability.

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Section: Mathematical Modelling Of Inverse Problemmentioning

confidence: 99%

“…In eq (12), T is the total iteration. The constriction factor is calculated as follows when phi=4.1; This constriction factor is used to modify inertial weight.…”

Section: Selection Of Inertia Constantmentioning

confidence: 99%

Electrical Impedance Tomography – Image Reconstruction using Population-based Optimisation Algorithms

Khan¹,

Ling

Rizvi

2022

Preprint

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“…Optimization by particle swarms Newton's algorithm, which is used to solve the inverse problem, is a very sensitive to the initial values. Thus, to overcome this drawback, Particulate Swarm Optimization (PSO) can be used [49]. A conductivity-based clustering algorithm, a modified Newton-Raphson algorithm, can also be combined with an adaptive PSO algorithm to improve the quality of the reconstructed image.…”

Section: Newton-raphsonmentioning

confidence: 99%

Electrical Impedance tomography – recent applications and developments

Mansouri

Alharbi

Haddad

et al. 2021

Journal of Electrical Bioimpedance

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Electrical impedance tomography (EIT) is a low-cost noninvasive imaging method. The main purpose of this paper is to highlight the main aspects of the EIT method and to review the recent advances and developments. The advances in instrumentation and in the different image reconstruction methods and systems are demonstrated in this review. The main applications of the EIT are presented and a special attention made to the papers published during the last years (from 2015 until 2020). The advantages and limitations of EIT are also presented. In conclusion, EIT is a promising imaging approach with a strong potential that has a large margin of progression before reaching the maturity phase.

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