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.
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.
Purpose: Due to multiple factors, children left behind in rural areas suffer from delayed neurodevelopment (suspected developmental delay, SDD). This study aimed to analyze the effect of caregivers’ depression on left-behind children’s SDD, with early stimulation and responsive care mediating. Methods: A cross-sectional survey was conducted in villages of five Chinese counties. A total of 904 left-behind children and their primary family caregivers were enrolled. Information on the children and their caregivers were collected through face-to-face interviews. The Zung Self-rating Depression Scale (ZSDS) was used to measure caregivers’ depressive symptoms. The Ages and Stages questionnaires, third edition (ASQ-3) was used to screen children for SDD, which contains five domains: communication (CM), gross motor (GM), fine motor (FM), problem-solving (CG), and personal social (PS). Results: Of left-behind children’s caregivers, 39.7% experienced varying symptoms of depression. The prevalence of total SDD among left-behind children under three years was 31.4%. Caregivers’ ZSDS scores were positively correlated with the FM, GM, CG, and PS of the left-behind children. Caregivers’ early stimulation & responsive care was positively correlated with the CM, FM, CG, and PS of the left-behind children. Conclusion: Left-behind children under three years in rural China were at high risk of SDD and their caregivers’ mental health was not good. Caregivers’ depressive symptoms may negatively affect the suspected developmental delay of left-behind children through caregivers providing less early stimulation and responsive care. A comprehensive intervention should be conducted to promote the health of left-behind children and their caregivers.
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 noncontact 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.
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