Impedance Tomography
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10.29007/x6vj ◽  
2022 ◽  
Author(s):  
Minh Quan Cao Dinh ◽  
Quoc Tuan Nguyen Diep ◽  
Hoang Nhut Huynh ◽  
Ngoc An Dang Nguyen ◽  
Anh Tu Tran ◽  
...  

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.


Author(s):  
Benoit Brazey ◽  
Yassine Haddab ◽  
Laure Koebel ◽  
Nabil Zemiti

Abstract The presence of a tumor in the tongue is a pathology that requires surgical intervention from a certain stage. This type of surgery is difficult to perform because of the limited space available around the base of the tongue for the insertion of surgical tools. During the procedure, the surgeon has to stretch and then fix the tongue firmly in order to optimize the available space and prevent tissue movement. As a result, the preoperative images of the inside of the tongue no longer give a reliable indication of the position and shape of the cancerous tissue due to the deformation of the overall tissue in the area. Thus, new images are needed during the operation, but are very difficult to obtain using conventional techniques due to the presence of surgical tools. Electrical Impedance Tomography (EIT) is an imaging technique that maps the resistivity or difference of resistivity of biological tissues from electrical signals. The small size of the electrodes makes it a potentially interesting tool to obtain intraoperative images of the inside of the tongue. In this paper, the possibility of using EIT for this purpose is investigated. A detection method is proposed, including an original configuration of the electrodes, consistent with the anatomical specificities of the tongue. The proposed method is studied in simulation and then a proof of concept is obtained experimentally on a 3D printed test tank filled with saline solution and plant fibres.


2022 ◽  
Vol 4 (4) ◽  
pp. 1-22
Author(s):  
Valentina Candiani ◽  
◽  
Matteo Santacesaria ◽  

<abstract><p>We consider the problem of the detection of brain hemorrhages from three-dimensional (3D) electrical impedance tomography (EIT) measurements. This is a condition requiring urgent treatment for which EIT might provide a portable and quick diagnosis. We employ two neural network architectures - a fully connected and a convolutional one - for the classification of hemorrhagic and ischemic strokes. The networks are trained on a dataset with $ 40\, 000 $ samples of synthetic electrode measurements generated with the complete electrode model on realistic heads with a 3-layer structure. We consider changes in head anatomy and layers, electrode position, measurement noise and conductivity values. We then test the networks on several datasets of unseen EIT data, with more complex stroke modeling (different shapes and volumes), higher levels of noise and different amounts of electrode misplacement. On most test datasets we achieve $ \geq 90\% $ average accuracy with fully connected neural networks, while the convolutional ones display an average accuracy $ \geq 80\% $. Despite the use of simple neural network architectures, the results obtained are very promising and motivate the applications of EIT-based classification methods on real phantoms and ultimately on human patients.</p></abstract>


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

2021 ◽  
Vol 8 ◽  
Author(s):  
Sébastien Gibot ◽  
Marie Conrad ◽  
Guilhem Courte ◽  
Aurélie Cravoisy

Introduction: The best way to titrate the positive end-expiratory pressure (PEEP) in patients suffering from acute respiratory distress syndrome is still matter of debate. Electrical impedance tomography (EIT) is a non-invasive technique that could guide PEEP setting based on an optimized ventilation homogeneity.Methods: For this study, we enrolled the patients with 2019 coronavirus disease (COVID-19)-related acute respiratory distress syndrome (ARDS), who required mechanical ventilation and were admitted to the ICU in March 2021. Patients were monitored by an esophageal catheter and a 32-electrode EIT device. Within 48 h after the start of mechanical ventilation, different levels of PEEP were applied based upon PEEP/FiO2 tables, positive end-expiratory transpulmonary (PL)/ FiO2 table, and EIT. Respiratory mechanics variables were recorded.Results: Seventeen patients were enrolled. PEEP values derived from EIT (PEEPEIT) were different from those based upon other techniques and has poor in-between agreement. The PEEPEIT was associated with lower plateau pressure, mechanical power, transpulmonary pressures, and with a higher static compliance (Crs) and homogeneity of ventilation.Conclusion: Personalized PEEP setting derived from EIT may help to achieve a more homogenous distribution of ventilation. Whether this approach may translate in outcome improvement remains to be investigated.


2021 ◽  
Author(s):  
Xiaojie Wang ◽  
Haofeng Chen ◽  
Gang Ma ◽  
xuanxuan yang ◽  
jialu geng

In this paper, a large-area flexible tactile sensor for multi-touch and force detection based on EIT technology was developed. A novel design of a sensor material made of a porous elastic polymer and ionic liquid was proposed. The proposed conductive flexible materials combining elastic porous structures and conductive liquids provide continuous, linear changes in impedance with respect to touch forces. A deep learning scheme PSPNet based on MobileNet was adopted to postprocess the originally reconstructed images to improve the performance of tactile perception. By using this data-driven method, we can improve the spatial resolution of the tactile sensor to achieve a single-point position detection error of 7.5±4.5 mm without using internal electrodes.


2021 ◽  
Author(s):  
Xiaojie Wang ◽  
Haofeng Chen ◽  
Gang Ma ◽  
xuanxuan yang ◽  
jialu geng

In this paper, a large-area flexible tactile sensor for multi-touch and force detection based on EIT technology was developed. A novel design of a sensor material made of a porous elastic polymer and ionic liquid was proposed. The proposed conductive flexible materials combining elastic porous structures and conductive liquids provide continuous, linear changes in impedance with respect to touch forces. A deep learning scheme PSPNet based on MobileNet was adopted to postprocess the originally reconstructed images to improve the performance of tactile perception. By using this data-driven method, we can improve the spatial resolution of the tactile sensor to achieve a single-point position detection error of 7.5±4.5 mm without using internal electrodes.


2021 ◽  
Vol 90 (1) ◽  
Author(s):  
Francesco Colibazzi ◽  
Damiana Lazzaro ◽  
Serena Morigi ◽  
Andrea Samoré

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