scholarly journals Feasibility of Temperature Control by Electrical Impedance Tomography in Hyperthermia

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3297
Author(s):  
Redi Poni ◽  
Esra Neufeld ◽  
Myles Capstick ◽  
Stephan Bodis ◽  
Theodoros Samaras ◽  
...  
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Reference Model ◽  
Low Cost ◽  
Temperature Reconstruction ◽  
Voltage Measurement ◽  
Noninvasive Technique ◽  
Impedance Tomography ◽  
Reconstruction Methods ◽  
The Impact

We present a simulation study investigating the feasibility of electrical impedance tomography (EIT) as a low cost, noninvasive technique for hyperthermia (HT) treatment monitoring and adaptation. Temperature rise in tissues leads to perfusion and tissue conductivity changes that can be reconstructed in 3D by EIT to noninvasively map temperature and perfusion. In this study, we developed reconstruction methods and investigated the achievable accuracy of EIT by simulating HT treatmentlike scenarios, using detailed anatomical models with heterogeneous conductivity distributions. The impact of the size and location of the heated region, the voltage measurement signal-to-noise ratio, and the reference model personalization and accuracy were studied. Results showed that by introducing an iterative reconstruction approach, combined with adaptive prior regions and tissue-dependent penalties, planning-based reference models, measurement-based reweighting, and physics-based constraints, it is possible to map conductivity-changes throughout the heated domain, with an accuracy of around 5% and cm-scale spatial resolution. An initial exploration of the use of multifrequency EIT to separate temperature and perfusion effects yielded promising results, indicating that temperature reconstruction accuracy can be in the order of 1 ∘C. Our results suggest that EIT can provide valuable real-time HT monitoring capabilities. Experimental confirmation in real-world conditions is the next step.

Using Extreme Learning Machines and the Backprojection Algorithm as an Alternative to Reconstruct Electrical Impedance Tomography Images

2021 ◽  
pp. 16-27
Author(s):  
Juliana Carneiro Gomes ◽  
Maíra Araújo de Santana ◽  
Clarisse Lins de Lima ◽  
Ricardo Emmanuel de Souza ◽  
Wellington Pinheiro dos Santos
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Imaging Techniques ◽  
Low Cost ◽  
Computational Cost ◽  
Extreme Learning Machines ◽  
Impedance Tomography ◽  
Learning Machines ◽  
Electrical Potentials ◽  
Reconstruction Methods

Electrical Impedance Tomography (EIT) is an imaging technique based on the excitation of electrode pairs applied to the surface of the imaged region. The electrical potentials generated from alternating current excitation are measured and then applied to boundary-based reconstruction methods. When compared to other imaging techniques, EIT is considered a low-cost technique without ionizing radiation emission, safer for patients. However, the resolution is still low, depending on efficient reconstruction methods and low computational cost. EIT has the potential to be used as an alternative test for early detection of breast lesions in general. The most accurate reconstruction methods tend to be very costly as they use optimization methods as a support. Backprojection tends to be rapid but more inaccurate. In this work, the authors propose a hybrid method, based on extreme learning machines and backprojection for EIT reconstruction. The results were applied to numerical phantoms and were considered adequate, with potential to be improved using post processing techniques.

scholarly journals Electrical Impedance tomography – recent applications and developments

2021 ◽  
Vol 12 (1) ◽  
pp. 50-62
Author(s):  
Sofiene Mansouri ◽  
Yousef Alharbi ◽  
Fatma Haddad ◽  
Souhir Chabcoub ◽  
Anwar Alshrouf ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Low Cost ◽  
Noninvasive Imaging ◽  
Imaging Method ◽  
Impedance Tomography ◽  
Large Margin ◽  
Reconstruction Methods ◽  
Imaging Approach

Abstract 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.

scholarly journals Effect of the number of electrodes on the reconstructed lung shape in electrical impedance tomography

2016 ◽  
Vol 2 (1) ◽  
pp. 499-502 ◽  
Author(s):  
Benjamin Schullcke ◽  
Sabine Krueger-Ziolek ◽  
Bo Gong ◽  
Knut Moeller
Keyword(s):  
Image Quality ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Regional Distribution ◽  
Current Injection ◽  
The Body ◽  
Voltage Measurement ◽  
Impedance Tomography ◽  
The Impact ◽  
Stimulation Pattern

AbstractElectrical impedance tomography (EIT) is used to monitor the regional distribution of ventilation in a transversal plane of the thorax. In this manuscript we evaluate the impact of different quantities of electrodes used for current injection and voltage measurement on the reconstructed shape of the lungs. Results indicate that the shape of reconstructed impedance changes in the body depends on the number of electrodes. In this manuscript, we demonstrate that a higher number of electrodes do not necessarily increase the image quality. For the used stimulation pattern, utilizing neighboring electrodes for current injection and voltage measurement, we conclude that the shape of the lungs is best reconstructed if 16 electrodes are used.

CALIBRATION OF A LOW COST ELECTRICAL IMPEDANCE TOMOGRAPHY SYSTEM WITH A RING OF RESISTORS AND A KCL SOLUTION

2019 ◽  
Author(s):  
Bruno Furtado de Moura ◽  
francisco sepulveda ◽  
Jorge Luis Jorge Acevedo ◽  
Wellington Betencurte da Silva ◽  
Rogerio Ramos ◽  
...  
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Low Cost ◽  
Impedance Tomography ◽  
Tomography System

A Methodology to Assist in Improvements of Low-cost Electrical Impedance Tomography Systems

2022 ◽  
Vol 20 (1) ◽  
pp. 141-152
Author(s):  
Bruno Furtado De Moura ◽  
Adriana Machado Malafaia Da Mata ◽  
Marcio Ferreira Martins ◽  
Francisco Hernan Sepulveda Palma ◽  
Rogerio Ramos
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Low Cost ◽  
Impedance Tomography

scholarly journals Low Cost Instrumentation for Electrical Impedance Tomography in Biomedical Applications

2015 ◽  
Author(s):  
Stewart Smith ◽  
Hancong Wu ◽  
Jiabin Jia
Keyword(s):  
Electrical Impedance Tomography ◽  
Integrated Circuit ◽  
Biomedical Applications ◽  
Electrical Impedance ◽  
Low Cost ◽  
Impedance Measurement ◽  
Impedance Tomography ◽  
Impedance Measurements ◽  
Additional Circuitry ◽  
Initial Results

This poster reports the design, implementation and testing of a portable and inexpensive bio-impedance measurement system intended for electrical impedance tomography (EIT) in cell cultures. The system is based on the AD5933 impedance analyser integrated circuit with additional circuitry to enable four-terminal measurement. Initial results of impedance measurements are reported along with an EIT image reconstructed using the open source EIDORS package.

scholarly journals Development a low-cost electrical impedance tomography system for non-invasively detecting abnormal in breast tissue - Phantom study-

10.29007/x6vj ◽  
2022 ◽  
Author(s):  
Minh Quan Cao Dinh ◽  
Quoc Tuan Nguyen Diep ◽  
Hoang Nhut Huynh ◽  
Ngoc An Dang Nguyen ◽  
Anh Tu Tran ◽  
...  
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Low Cost ◽  
Impedance Tomography ◽  
Non Invasive ◽  
Tomography System ◽  
Tissue Phantom ◽  
Cost System ◽  
Invasive Method ◽  
Breast Tissues

Electrical Impedance Tomography (EIT) is known as non-invasive method to detect and classify the abnormal breast tissues. Reimaging conductivity distribution within an area of the subject reveal abnormal tissues inside that area. In this work, we have created a very low-cost system with a simple 16-electrode phantom for doing research purposes. The EIT data were measured and reconstructed with EIDORS software.

A novel deep neural network method for electrical impedance tomography

2019 ◽  
Vol 41 (14) ◽  
pp. 4035-4049 ◽  
Author(s):  
Xiuyan Li ◽  
Yong Zhou ◽  
Jianming Wang ◽  
Qi Wang ◽  
Yang Lu ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Simulation Software ◽  
Voltage Measurement ◽  
Finite Elements Analysis ◽  
Impedance Tomography ◽  
Conductivity Distribution ◽  
Highly Nonlinear ◽  
Internal Conductivity

Image reconstruction for Electrical Impedance Tomography (EIT) is a highly nonlinear and ill-posed inverse problem. It requires the design and employment of feasible reconstruction methods capable to guarantee trustworthy image generation. Deep Neural Networks (DNN) have a powerful ability to express complex nonlinear functions. This research paper introduces a novel framework based on DNN aiming to achieve EIT image reconstruction. The proposed DNN model, comprises of the following two layers, namely: The Stacked Autoencoder (SAE) and the Logistic Regression (LR). It is trained using the large lab samples which are obtained by the COMSOL simulation software (a cross platform finite elements analysis solver). The relationship between the voltage measurement and the internal conductivity distribution is determined. The untrained voltage measurement samples are used as input to the trained DNN, and the output is an estimate for image reconstruction of the internal conductivity distribution. The results show that the proposed model can achieve reliable shape and size reconstruction. When white Gaussian noise with a signal-to-noise ratio of 30, 40 and 50 were added to test set, the proposed DNN structure still has good imaging results, which proved the anti-noise capability of the network. Furthermore, the network that was trained using simulation data sets, would be applied for the EIT image reconstruction based on the experimental data that were produced after preprocessing.

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