A 3D multi-frequency response electrical mesh phantom for validation of the planar structure EIT system performance

Assessment and validation of the Electrical Impedance Tomography (EIT) system performance and calibration of systematic errors in the electrical field generated inside of the interrogated volume is an important requirement. System instabilities can be caused by the EIT design and must be characterized before and during the clinical trials. Evaluation of the Sussex EIT system used in the clinical study can be based on a realistic electronic phantom. We designed a mesh phantom based on the electrode configuration and mesh structures of the image reconstruction. The phantom has the capability of modelling the cellular electrical properties that are operative within a circular homogeneous medium. The design is optimized to assess the planar topology of the internal impedance distribution. The system employs the information from the electrical properties of biological tissues to evaluate the Cole-Cole dispersion data. This mesh phantom is capable of producing localized conductivity perturbations between each arbitrary channel in the electrode placement planar phantom topology by measuring all 1416 combinations that are to be used in the image reconstruction. The phantom is especially designed for the Sussex EIT system to validate system performance of measurements consisting of SNR, and modelling system accuracy

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

FPGA technology in process tomography

The aims of this paper are to provide a review of the process tomography applications employing field programmable gate arrays (FPGA) and to understand current FPGA related researches, in order to seek for the possibility to applied FPGA technology in an ultrasonic process tomography system. FPGA allows users to implement complete systems on a programmable chip, meanwhile, five main benefits of applying the FPGA technology are performance, time to market, cost, reliability, and long-term maintenance. These advantages definitely could help in the revolution of process tomography, especially for ultrasonic process tomography and electrical process tomography. Future work is focused on the ultrasonic process tomography for chemical process column investigation using FPGA for the aspects of low cost, high speed and reconstructed image quality

Advances in Integrated Circuits and Systems for Wearable Biomedical Electrical Impedance Tomography

Electrical impedance tomography (EIT) is an impedance mapping technique that can be used to image the inner impedance distribution of the subject under test. It is non-invasive, inexpensive and radiation-free, while at the same time it can facilitate long-term and real-time dynamic monitoring. Thus, EIT lends itself particularly well to the development of a bio-signal monitoring/imaging system in the form of wearable technology. This work focuses on EIT system hardware advancement using complementary metal oxide semiconductor (CMOS) technology. It presents the design and testing of application specific integrated circuit (ASIC) and their successful use in two bio-medical applications, namely, neonatal lung function monitoring and human-machine interface (HMI) for prosthetic hand control. Each year fifteen million babies are born prematurely, and up to 30% suffer from lung disease. Although respiratory support, especially mechanical ventilation, can improve their survival, it also can cause injury to their vulnerable lungs resulting in severe and chronic pulmonary morbidity lasting into adulthood, thus an integrated wearable EIT system for neonatal lung function monitoring is urgently needed. In this work, two wearable belt systems are presented. The first belt features a miniaturized active electrode module built around an analog front-end ASIC which is fabricated with 0.35-µm high-voltage process technology with ±9 V power supplies and occupies a total die area of 3.9 mm². The ASIC offers a high power active current driver capable of up to 6 mAp-p output, and wideband active buffer for EIT recording as well as contact impedance monitoring. The belt has a bandwidth of 500 kHz, and an image frame rate of 107 frame/s. To further improve the system, the active electrode module is integrated into one ASIC. It contains a fully differential current driver, a current feedback instrumentation amplifier (IA), a digital controller and multiplexors with a total die area of 9.6 mm². Compared to the conventional active electrode architecture employed in the first EIT belt, the second belt features a new architecture. It allows programmable flexible electrode current drive and voltage sense patterns under simple digital control. It has intimate connections to the electrodes for the current drive and to the IA for direct differential voltage measurement providing superior common-mode rejection ratio (CMRR) up to 74 dB, and with active gain, the noise level can be reduced by a factor of √3 using the adjacent scan. The second belt has a wider operating bandwidth of 1 MHz and multi-frequency operation. The image frame rate is 122 frame/s, the fastest wearable EIT reported to date. It measures impedance with 98% accuracy and has less than 0.5 Ω and 1° variation across all channels. In addition the ASIC facilitates several other functionalities to provide supplementary clinical information at the bedside. With the advancement of technology and the ever-increasing fusion of computer and machine into daily life, a seamless HMI system that can recognize hand gestures and motions and allow the control of robotic machines or prostheses to perform dexterous tasks, is a target of research. Originally developed as an imaging technique, EIT can be used with a machine learning technique to track bones and muscles movement towards understanding the human user’s intentions and ultimately controlling prosthetic hand applications. For this application, an analog front-end ASIC is designed using 0.35-µm standard process technology with ±1.65 V power supplies. It comprises a current driver capable of differential drive and a low noise (9μVrms) IA with a CMRR of 80 dB. The function modules occupy an area of 0.07 mm². Using the ASIC, a complete HMI system based on the EIT principle for hand prosthesis control has been presented, and the user’s forearm inner bio-impedance redistribution is assessed. Using artificial neural networks, bio-impedance redistribution can be learned so as to recognise the user’s intention in real-time for prosthesis operation. In this work, eleven hand motions are designed for prosthesis operation. Experiments with five subjects show that the system can achieve an overall recognition accuracy of 95.8%

Quantitative MR Imaging of the Electric Properties and Local SAR based on Improved RF Transmit Field Mapping

This work presents three new quantitative methods for magnetic resonance imaging. A method for simultaneous mapping of B1 and T1 (MTM) is developed and validated. Electric Properties Tomography (EPT), a method for quantitative imaging of dielectric properties of tissue, is presented. Based on EPT, separate (phase-based) conductivity and (amplitude-based) permittivity measurements are introduced. Finally, a B1-based method for patient-specific local SAR measurements is presented

Exploring the Potential of Electrical Impedance Tomography for Tissue Engineering Applications

In tissue engineering, cells are generally cultured in biomaterials to generate three-dimensional artificial tissues to repair or replace damaged parts and re-establish normal functions of the body. Characterizing cell growth and viability in these bioscaffolds is challenging, and is currently achieved by destructive end-point biological assays. In this study, we explore the potential to use electrical impedance tomography (EIT) as a label-free and non-destructive technology to assess cell growth and viability. The key challenge in the tissue engineering application is to detect the small change of conductivity associated with sparse cell distributions in regards to the size of the hosting scaffold, i.e., low volume fraction, until they assemble into a larger tissue-like structure. We show proof-of-principle data, measure cells within both a hydrogel and a microporous scaffold with an ad-hoc EIT equipment, and introduce the frequency difference technique to improve the reconstruction

Electrical impedance tomography for real-time 3D tissue culture monitoring

Electrical impedance tomography (EIT) is an emerging image technique that can image the spatial conductivity distribution in the sensing area by generating an electric field and measuring the induced boundary voltages. With the advantages of low-cost, high-temporal resolution, non-destructive and non-radiative, EIT has been developed for the clinical applications, including thorax imaging, lung ventilation monitoring, breast cancer screening and functional brain imaging. Its feasibility for monitoring the motion and conductivity change of the human tissues has been well investigated. It, therefore, shows enormous potential in the in-vitro cellular characterisation, where samples have the same electrical properties as in-vivo human tissues. Since conventional biological imaging techniques are mainly optimised for the monolayer cell culture, their performance is limited when processing the dense, highly scattering tissues, which can better mimic the in vivo situation than 2D cultured cells. Utilising EIT as a novel method to monitor these 3D samples may help to overcome the difficulties and improve the temporal resolution of the data. This thesis aims to evaluate the feasibility of miniature EIT for 3D sample imaging and improve its performance for real-time 3D tissue culture monitoring. Phantom studies were first carried out to evaluate the challenges of EIT imaging when performing in the sensors in the millimetre scale. Different imaging settings, including imaging modality and measuring frequency, were compared, and a combined regularisation method is proposed to improve the image quality. Besides, a physical model for 3D biological tissue was developed to estimate its equivalent conductivity through the electrical properties and volume fraction of cells. The spatial resolution of EIT for tissue culture imaging was examined based on the model. In addition, the protocols of time-difference and frequency-difference EIT for 3D tissue culture monitoring in tightly packed spheroids and sparsely distributed bioscaffolds have been developed and verified through the experiments utilising MCF-7 breast cancer cells. Moreover, equivalent circuit models were developed for the EIT measurement, and a joint simulation method combining the finite element model and equivalent circuit analysis was developed to analyse the measurement error in frequency-difference EIT. Finally, a calibration method was developed to eliminate the circuitry errors in frequency-difference EIT so that it can be applied for the long-term monitoring in biological applications. In summary, this thesis presents the research works on improving the robustness of miniature EIT to the measuring noise and the background disturbance through the optimization of experimental protocols, measuring methods and imaging settings. It shows the potential to be applied in biological research using 3D cell culture, including drug discovery and tissue engineering

Detection of breast cancer with electrical impedance mammography

Electrical Impedance Tomography (EIT) is a medical imaging technique that reconstructs internal electrical conductivity distribution of a body from impedance data that is measured on the body surface, and Electrical Impedance Mammography (EIM) is the technique that applies EIT in breast cancer detection. The use of EIM for breast cancer identification is highly desirable because it is a non-invasive and low-cost imaging technology. EIM has the potential in detecting early stage cancer, however there are still challenges that hindering EIM to be provided as a routine health care system. There are three major groups of obstacles. One is the hardware design, which includes the selection of electronic components, electrode-skin contacting methods, etc. Second is theoretical problems such as electrode configurations, image reconstruction and regularization methods. Third is the development of analysis methods and generation of a cancerous tissue database. Research reported in this thesis strives to understand these problems and aims to provide possible solutions to build a clinical EIM system. The studies are carried out in four parts. First the functionalities of the Sussex Mk4 EIM system have been studied. Sensitivity of the system was investigated to find out the strength and weakness of the system. Then work has been made on image reconstruction and regularization methods in order to enhance the system’s endurance to noise, also to balance the reconstruction conductivity distribution throughout the reconstructed object. Then a novel cancer diagnosis technique was proposed. It was developed based on the electrical property of human breast tissue and the behaviour or systematic noise, to provide repeatable results for each patient. Finally evaluation has been made on previous EIM systems to find out the major problems. Based on sensitivity analysis, an optimal combined electrode configuration has been proposed to improve sensitivity. The system has been developed and produced meaningful clinical images. The work makes significant contributions to society. This novel cancer diagnosis method has high accuracy for cancer identification. The combined electrode configuration has also provided flexibilities in the designing of current driving and voltage receiving patterns, thus sensitivity of the EIM system can be greatly improved