Selected Papers from the 9th World Congress on Industrial Process Tomography

Industrial process tomography (IPT) is becoming an important tool for Industry 4.0. It consists of multidimensional sensor technologies and methods that aim to provide unparalleled internal information on industrial processes used in many sectors. This book showcases a selection of papers at the forefront of the latest developments in such technologies

An Imaged-Based Method for Universal Performance Evaluation of Electrical Impedance Tomography Systems

This paper describes a simple and reproducible methodology for universal evaluation of the performance of electrical impedance tomography (EIT) systems using reconstructed images. Based on objective full referencing (FR), the method provides a visually distinguishable hot colormap and two new FR metrics, the global and the more specific region of interest, to address the issues where common electrical parameters are not directly related to the quality of EIT images. A passive 16 electrode EIT system using an application specific integrated circuit front-end was used to evaluate the proposed method. The measured results show, both visually and in terms of the proposed FR metrics, the impact on recorded EIT images with different design parameters and non-idealities. The paper also compares the image results of a passive electrode system with a matched single variable active electrode system and demonstrates the merit of an active electrode system for noise interference

Conditioning electrical impedance mammography system

A multi-frequency Electrical Impedance Mammography (EIM) system has been developed to evaluate the conductivity and permittivity spectrums of breast tissues, which aims to improve early detection of breast cancer as a non-invasive, relatively low cost and label-free screening (or pre-screening) method. Multi-frequency EIM systems typically employ current excitations and measure differential potentials from the subject under test. Both the output impedance and system performance (SNR and accuracy) depend on the total output resistance, stray and output capacitances, capacitance at the electrode level, crosstalk at the chip and PCB levels. This makes the system design highly complex due to the impact of the unwanted capacitive effects, which substantially reduce the output impedance of stable current sources and bandwidth of the data that can be acquired. To overcome these difficulties, we present new methods to design a high performance, wide bandwidth EIM system using novel second generation current conveyor operational amplifiers based on a gyrator (OCCII-GIC) combination with different current excitation systems to cancel unwanted capacitive effects from the whole system. We reconstructed tomography images using a planar E-phantom consisting of an RSC circuit model, which represents the resistance of extra-cellular (R), intra-cellular (S) and membrane capacitance (C) of the breast tissues to validate the performance of the system. The experimental results demonstrated that an EIM system with the new design achieved a high output impedance of 10MΩ at 1MHz to at least 3MΩ at 3MHz frequency, with an average SNR and modelling accuracy of over 80dB and 99%, respectively

Tecniche Elettrotomografiche per la caratterizzazione dei tessuti biologici

Electrical impedance tomography (EIT) is an imaging modality wherein the spatial map of conductivity and permittivity inside a medium is obtained from a set of surface electrical measurements. Electrodes are brought into contact with the surface of the object being imaged and a set of currents are applied and the corresponding voltages are measured. These voltages and currents are then used to estimate the electrical properties of the object using an image reconstruction algorithm which relies on an accurate model of the electrical interaction. The process of property estimation, called inverse problem, is highly ill-posed and it requires a Regularization method. The objective of this Thesis was to develop a device for imaging using the EIT technique, which was convenient, noninvasive, easily programmable, portable and relatively cheap in contrast to many other diagnostic tool. In this direction a simple EIT system and its hardware and software parts are developed. The data processing was accomplished by utilizing the EIDORS toolkit, which was developed for application to this nonlinear and ill-posed inverse problem. Experiments have indicated that the EIT system can reconstruct resistive and capacitive images of good contrast despite errors in the measurement are not taken in account

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%