Regional admittivity reconstruction with multi-frequency complex admittance data using contactless capacitive electrical tomography

Tomographic imaging of the electrical properties distribution within biological subjects such as the human body has been an active research goal in electrical tomography (ET). As the electrical properties of a living tissue vary with the excitation frequency, measuring the frequency-dependent behaviour of the effective dielectric can increase the possibilities for tissue characterisation, and thus enhance the potential for extended clinical applications. The ET system generally enables to capture the changes in effective dielectric properties at low spatial resolution, therefore, the complete complex admittance spectrum can be reconstructed by ET to enrich the information content and further provide better diagnostic. In this work, we demonstrate a novel contactless ET system which relies on the capacitive coupled principle, the capacitive coupled electrical tomography (CCET). Except the non-contact measuring characteristic, the capacitance-based imaging principle enables the system to obtain the measurements at higher excitation frequencies. These characteristics give CCET great potential in future medical application, as the high-frequency component of complex impedance plays a dominant role in establishing the link between the microscopic cell structures and the macroscopic admittivity images obtained from multi-frequency ET systems. In this paper, we used multi-frequency electrical signals from 320 kHz to 14 MHz to conduct the single and multiple inclusions test with different biological samples. Both the reconstructed tomographic images and the Cole-Cole plots confirm the ability of CCET in characterising different objects.</p

A New Label-Free and Contactless Bio-Tomographic Imaging with Miniaturized Capacitively-Coupled Spectroscopy Measurements

A new bio-imaging method has been developed by introducing an experimental verification of capacitively coupled resistivity imaging in a small scale. This paper focuses on the 2D circular array imaging sensor as well as a 3D planar array imaging sensor with spectroscopic measurements in a wide range from low frequency to radiofrequency. Both these two setups are well suited for standard containers used in cell and culture biological studies, allowing for fully non-invasive testing. This is true as the capacitive based imaging sensor can extract dielectric spectroscopic images from the sample without direct contact with the medium. The paper shows the concept by deriving a wide range of spectroscopic information from biological test samples. We drive both spectra of electrical conductivity and the change rate of electrical conductivity with frequency as a piece of fundamentally important information. The high-frequency excitation allows the interrogation of critical properties that arise from the cell nucleus.</p

Magnetic induction tomography methods and applications:a review

Magnetic induction tomography (MIT) is a tomographic technique capable of imaging the passive electromagnetic properties of an object. It has the advantages of being contact-less and non-invasive, as the process involves interrogating the electromagnetic field of the imaging subject. As such, the potential applications of MIT are broad, with various domains of operation including biomedicine, industrial process tomography and non-destructive evaluation. Consequently, there is a rich—yet underexplored—research landscape for the practical applications of MIT. The aim of this review is to provide a non-exhaustive overview of this landscape. The fundamental principles of MIT are discussed, alongside the instrumentation and techniques necessary to obtain and interpret MIT measurements

A hybrid image reconstruction for medical magnetic induction tomography:an experimental evaluation

Belgium Herbarium image of Meise Botanic Garden

Application of deep neural network to the reconstruction of two-phase material imaging by capacitively coupled electrical resistance tomography

A convolutional neural network (CNN)-based image reconstruction algorithm for two-phase material imaging is presented and verified with experimental data from a capacitively coupled electrical resistance tomography (CCERT) sensor. As a contactless version of electrical resistance tomography (ERT), CCERT has advantages such as no invasion, low cost, no radiation, and rapid response for two-phase material imaging. Besides that, CCERT avoids contact error of ERT by imaging from outside of the pipe. Forward modeling was implemented based on the practical circular array sensor, and the inverse image reconstruction was realized by a CNN-based supervised learning algorithm, as well as the well-known total variation (TV) regularization algorithm for comparison. The 2D, monochrome, 2500-pixel image was divided into 625 clusters, and each cluster was used individually to train its own CNN to solve the 16 classes classification problem. Inherent regularization for the assumption of binary materials enabled us to use a classification algorithm with CNN. The iterative TV regularization algorithm achieved a close state of the two-phase material reconstruction by its sparsity-based assumption. The supervised learning algorithm established the mathematical model that mapped the simulated resistance measurement to the pixel patterns of the clusters. The training process was carried out only using simulated measurement data, but simulated and experimental tests were both conducted to investigate the feasibility of applying a multi-layer CNN for CCERT imaging. The performance of the CNN algorithm on the simulated data is demonstrated, and the comparison between the results created by the TV-based algorithm and the proposed CNN algorithm with the real-world data is also provided

Metal Solidification Imaging Process by Magnetic Induction Tomography

Abstract There are growing number of important applications that require a contactless method for monitoring an object surrounded inside a metallic enclosure. Imaging metal solidification is a great example for which there is no real time monitoring technique at present. This paper introduces a technique - magnetic induction tomography - for the real time in-situ imaging of the metal solidification process. Rigorous experimental verifications are presented. Firstly, a single inductive coil is placed on the top of a melting wood alloy to examine the changes of its inductance during solidification process. Secondly, an array of magnetic induction coils are designed to investigate the feasibility of a tomographic approach, i.e., when one coil is driven by an alternating current as a transmitter and a vector of phase changes are measured from the remaining of the coils as receivers. Phase changes are observed when the wood alloy state changes from liquid to solid. Thirdly, a series of static cold phantoms are created to represent various liquid/solid interfaces to verify the system performance. Finally, a powerful temporal reconstruction method is applied to realise real time in-situ visualisation of the solidification and the measurement of solidified shell thickness, a first report of its kind