Review on electrical impedance tomography: Artificial intelligence methods and its applications

© 2019 by the authors. Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented

Population-Based Advanced Optimisation Algorithms for Electrical Impedance Tomography Image Reconstruction

University of Technology Sydney. Faculty of Engineering and Information Technology.The necessity of preventing living tissues' direct exposure to ionising radiation has resulted in tremendous growth in the area of medical imaging and e-health, enhancing intensive care of perilous patients, and help to improve quality of life. Moreover, the practice of image-reconstruction instruments that utilise ionising radiation has a significant impact on the health of the patients. Long or frequent exposure to ionizing radiation is linked to several illnesses like Cancer. These factors urged to enhance the endeavours to advance non-invasive approaches, for instance, Electrical Impedance Tomography (EIT) which is a portable, non-invasive, low-cost, and safe imaging method. Nevertheless, 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 to the conventional methods that usually create low-resolution blurry images. EIT reconstruction techniques work on the principle of optimising the relative error of reconstruction utilising population-based optimisation methods that have been 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 utilizing 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 optimized 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 optimization methods. Results obtained from the proposed methods are quantitatively assessed with ground truth images by using the relative mean squared error, confirming that a low error value is reached in the results. HGSPSO algorithm has superior performance as compared to the other proposed methods in terms of solution quality and stability

Microwave Tomography Using Stochastic Optimization And High Performance Computing

This thesis discusses the application of parallel computing in microwave tomography for detection and imaging of dielectric objects. The main focus is on microwave tomography with the use of a parallelized Finite Difference Time Domain (FDTD) forward solver in conjunction with non-linear stochastic optimization based inverse solvers. Because such solvers require very heavy computation, their investigation has been limited in favour of deterministic inverse solvers that make use of assumptions and approximations of the imaging target. Without the use of linearization assumptions, a non-linear stochastic microwave tomography system is able to resolve targets of arbitrary permittivity contrast profiles while avoiding convergence to local minima of the microwave tomography optimization space. This work is focused on ameliorating this computational load with the use of heavy parallelization. The presented microwave tomography system is capable of modelling complex, heterogeneous, and dispersive media using the Debye model. A detailed explanation of the dispersive FDTD is presented herein. The system uses scattered field data due to multiple excitation angles, frequencies, and observation angles in order to improve target resolution, reduce the ill-posedness of the microwave tomography inverse problem, and improve the accuracy of the complex permittivity profile of the imaging target. The FDTD forward solver is parallelized with the use of the Common Unified Device Architecture (CUDA) programming model developed by NVIDIA corporation. In the forward solver, the time stepping of the fields are computed on a Graphics Processing Unit (GPU). In addition the inverse solver makes use of the Message Passing Interface (MPI) system to distribute computation across multiple work stations. The FDTD method was chosen due to its ease of parallelization using GPU computing, in addition to its ability to simulate wideband excitation signals during a single forward simulation. We investigated the use of distributed Particle Swarm Optimization (PSO) and Differential Evolution (DE) methods in the inverse solver for this microwave tomography system. In these optimization algorithms, candidate solutions are farmed out to separate workstations to be evaluated. As fitness evaluations are returned asynchronously, the optimization algorithm updates the population of candidate solutions and gives new candidate solutions to be evaluated to open workstations. In this manner, we used a total of eight graphics processing units during optimization with minimal downtime. Presented in this thesis is a microwave tomography algorithm that does not rely on linearization assumptions, capable of imaging a target in a reasonable amount of time for clinical applications. The proposed algorithm was tested using numerical phantoms that with material parameters similar to what one would find in normal or malignant human tissue

Machine learning approach to EIT image reconstruction of the human forearm section for different hand signs

Electrical impedance tomography (EIT) is an imaging technique used to reconstruct the conductivity of a target object from boundary voltages. In this study, we investigate suitable image reconstruction algorithms for EIT to enable the reconstruction of the conductivity distribution in the forearm section inferring muscle contractions at different hand signs. As EIT image reconstruction is an ill-posed inverse problem, the Gauss-Newton algorithm needs many iterations for the determination of suitable values of the regularization parameter and corresponding calculations of the Jacobian matrix. To reduce computational effort, we propose to use machine learning algorithms to directly reconstruct the EIT image. We explore the Radial Basis Neural Network (RBNN) and a one-dimensional Convolutional Neural Network (1D-CNN), which has been trained based on the measured EIT data for eight subjects, ten hand signs with ten trials. Both methods reach a low deviation at 0.0017 for RBNN and 0.0109 for CNN

Tactile perception in hydrogel-based robotic skins using data-driven electrical impedance tomography

Combining functional soft materials with electrical impedance tomography is a promising method for developing continuum sensorized soft robotic skins with high resolutions. However, reconstructing the tactile stimuli from surface electrode measurements is a challenging ill-posed modelling problem, with FEM and analytic models facing a reality gap. To counter this, we propose and demonstrate a model-free superposition method which uses small amounts of real-world data to develop deformation maps of a soft robotic skin made from a self-healing ionically conductive hydrogel, the properties of which are affected by temperature, humidity, and damage. We demonstrate how this method outperforms a traditional neural network for small datasets, obtaining an average resolution of 12.1 mm over a 170 mm circular skin. Additionally, we explore how this resolution varies over a series of 15,000 consecutive presses, during which damages are continuously propagated. Finally, we demonstrate applications for functional robotic skins: damage detection/localization, environmental monitoring, and multi-touch recognition - all using the same sensing material