Image Reconstruction for Electrical Impedance Tomography: Experimental Comparison of Radial Basis Neural Network and Gauss – Newton Method

Hrabuska, Radek; Prauzek, Michal; Venclikova, Marketa; Konecny, Jaromír

doi:10.1016/j.ifacol.2018.07.114

Cited by 16 publications

(10 citation statements)

References 19 publications

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“…These evolutionary algorithms work by reducing the root‐mean‐square errors between the analog and measured potentials. Table summarizes how different authors use these different algorithms to solve the EIT image reconstruction, such as improved algorithms, evolutionary algorithms, and intelligent algorithms …”

Section: Eit For Artificial Sensitive Skinsmentioning

confidence: 99%

Artificial Sensitive Skin for Robotics Based on Electrical Impedance Tomography

Wang

et al. 2020

Advanced Intelligent Systems

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Electrical impedance tomography (EIT) is a noninvasive measurement technique that estimates the internal resistivity distribution based on the boundary voltage–current data measured from the surface of the conductor. If a thin stretchable soft material with a certain piezoresistive property is used as the electrical conductor, EIT has the ability to reconstruct the position where the resistivity changes due to the inside pressure contact, so that a large‐scale artificial sensitive skin is provided for robotics. First, the different conduction principles and material types of artificial sensitive skins are discussed, which is next followed by the different driving modes and image reconstruction techniques. Then, details on how EIT is used for robotic skin applications are described. Finally, the development trends and future potentials of EIT‐based robotic skins are expounded.

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Section: Eit For Artificial Sensitive Skinsmentioning

confidence: 99%

Artificial Sensitive Skin for Robotics Based on Electrical Impedance Tomography

Wang

et al. 2020

Advanced Intelligent Systems

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“…In addition to the joint reconstruction using deep learning and traditional algorithms, hybrid deep learning reconstruction for EIT is a popular way of deep learning in EIT reconstruction. Hrabuska et al (2018) reported that first using radial basis neural network reconstruction and subsequently using a Hopfield neural network to filter the image will obtain better reconstruction results through simulation experiments. Subsequently, Huang et al (2019) proposed a method combining an ANN and U-Net for EIT image reconstruction.…”

Section: Deep Learning In Eit Image Reconstructionmentioning

confidence: 99%

Advances of deep learning in electrical impedance tomography image reconstruction

Zhang

Tian

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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Electrical impedance tomography (EIT) has been widely used in biomedical research because of its advantages of real-time imaging and nature of being non-invasive and radiation-free. Additionally, it can reconstruct the distribution or changes in electrical properties in the sensing area. Recently, with the significant advancements in the use of deep learning in intelligent medical imaging, EIT image reconstruction based on deep learning has received considerable attention. This study introduces the basic principles of EIT and summarizes the application progress of deep learning in EIT image reconstruction with regards to three aspects: a single network reconstruction, deep learning combined with traditional algorithm reconstruction, and multiple network hybrid reconstruction. In future, optimizing the datasets may be the main challenge in applying deep learning for EIT image reconstruction. Adopting a better network structure, focusing on the joint reconstruction of EIT and traditional algorithms, and using multimodal deep learning-based EIT may be the solution to existing problems. In general, deep learning offers a fresh approach for improving the performance of EIT image reconstruction and could be the foundation for building an intelligent integrated EIT diagnostic system in the future.

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“…They guess the conductivity distribution from the measurements after the learning phase. AIbased techniques are proficient of estimating the conductivity distribution in low computational time and, consequently, are appropriate for real-time applications [13]. After creating the data required for training that comprises noise and several artefacts, these algorithms can perform well in reconstructing the conductive distribution.…”

Section: Mathematical Modelling Of Inverse Problemmentioning

confidence: 99%

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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Image Reconstruction for Electrical Impedance Tomography: Experimental Comparison of Radial Basis Neural Network and Gauss – Newton Method

Cited by 16 publications

References 19 publications

Artificial Sensitive Skin for Robotics Based on Electrical Impedance Tomography

Artificial Sensitive Skin for Robotics Based on Electrical Impedance Tomography

Advances of deep learning in electrical impedance tomography image reconstruction

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

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