Early detection of breast cancer is key for a better survival rate, so there are huge demands for a safe and reliable breast cancer screening method, which currently is not available. Earlier studies are showing that the impedance spectroscopy can be an effective discriminator for breast tumor against healthy tissues. Traditional electrical impedance tomography (EIT) has progressed well into clinical stages for such an application with some promising results. But EIT generally operates at low frequencies, ranging typically lower than 1 MHz. Previous research work has suggested that the important information on the tumor impedance spectra exists in the higher frequency domain. Thus, this work focuses on the introduction and the evaluation of a spectral capacitively-coupled electrical resistivity tomography (CCERT) for breast cancer diagnosis. Compared to the traditional EIT, CCERT can reach a much higher frequency domain producing the spectroscopic images in a broader range. This in turns offers improved discrimination of tumors against normal tissues via frequency difference imaging or spectral imaging as it is shown in this paper. Additionally, direct contact between the EIT electrodes and the surface of the body, which causes significant challenges and uncertainties in measured data, is avoided in CCERT inherently a contactless method. In this paper, a 3D in-silico model of the CCERT system is created and solved using the finite element method (FEM) to simulate the forward model for each frequency. The feasibility simulation analysis is conducted based on the existing clinical breast cancer data and a CCERT reconstruction. For the experimental verification, an 8-electrode laboratory phantom was tested with the excitation frequency ranging from 200 kHz to 13 MHz. The simulation and experimental results show the promising potential of this method for breast cancer imaging. The wide-range spectroscopic images provide better discrimination of tumors against healthy tissues through their spectral conductivity profile as well as the rate of changes in spectral conductivity images. INDEX TERMS Breast cancer diagnosis, capacitively coupled electrical resistivity tomography (CCERT), conductivity spectrum of bio-samples, multi-frequency tomography.

There are growing interests in the use of robots in collaborative environments with humans or other intelligent machines. Sensing the environment for which the robot is operating can be done in many ways, generally guided by skin-like sensors. Some of the skins are inspired by natural sensing in humans or other species. As humans, we use many of our senses, such as version, hearing, smell, and touch, to move around by avoiding colliding with other humans or objects. Different from humans, many other mammals also use whiskers as an additional sensor to help navigate around. In this paper, we demonstrate a touchless capacitive imaging-based sensor in the situation where the obstacles are in close vicinity to the robot. The proposed imaging system can sense the changes in areas near to the skin-like sensors by measuring the capacitances between the array of electrodes. A 4D sensing approach has been developed with the spatiotemporal Total Variation algorithm. The 4D operational mode gives sensors the time awareness that allows for dynamical responses and hence the better control of the robots. Several experiments are conducted to show the skin-like behaviour of this sensor by simulating various scenarios. The sensor shows the excellent ability to detect an object in its vicinity, where the depth is close to half of the planar sensor array size.

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.

No abstract

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.