A very small value of inter-electrode capacitance is a main factor limiting image spatial resolution of electrical capacitance tomography. The front-end sensing circuit of multichannel capacitance tomography system EVT4 was designed for measurement of extremely small capacitance values in the range from 1 fF to several pF. The capacitance value is determined by charging it using a single high voltage pulse and integrating a charging current. The idea of gain adjustment in the front-end amplifier of a capacitance measurement circuit is presented. The switchable feedback loop of the inverting preamplifier-a first stage of the sensing circuit-enables gain adjustment in a wide range. The measurements using a cylindrical 2-D capacitance sensor were performed without and with gain adjustment. The obtained results showed improvement of signal-to-noise ratio for the pairs of opposite electrodes from about 22 to 30 dB.

A new method of capacitance measurement for electrical capacitance tomography is presented. A single-shot excitation is used to accelerate measurement. A high-voltage pulse and oversampling of received signal are applied to obtain an acceptable signal-to-noise ratio. The results of measurements of standard capacitors and mutual capacitance of electrodes in 16 electrode tomographic sensors are presented. The elaborated circuit is stray-immune. It can measure capacitance in a range from about 1 fF to 1 pF at one gain setting with good linearity and precision at the rate of 20 000 samples per second.

Electrical impedance tomography (EIT) is a non-invasive technique for visualizing the internal structure of a human body. Capacitively coupled electrical impedance tomography (CCEIT) is a new contactless EIT technique that can potentially be used as a wearable device. Recent studies have shown that a machine learning-based approach is very promising for EIT image reconstruction. Most of the studies concern models containing up to 22 electrodes and focus on using different artificial neural network models, from simple shallow networks to complex convolutional networks. However, the use of convolutional networks in image reconstruction with a higher number of electrodes requires further investigation. In this work, two different architectures of artificial networks were used for CCEIT image reconstruction: a fully connected deep neural network and a conditional generative adversarial network (cGAN). The training dataset was generated by the numerical simulation of a thorax phantom with healthy and illness-affected lungs. Three kinds of illnesses, pneumothorax, pleural effusion, and hydropneumothorax, were modeled using the electrical properties of the tissues. The thorax phantom included the heart, aorta, spine, and lungs. The sensor with 32 area electrodes was used in the numerical model. The ECTsim custom-designed toolbox for Matlab was used to solve the forward problem and measurement simulation. Two artificial neural networks were trained with supervision for image reconstruction. Reconstruction quality was compared between those networks and one-step algebraic reconstruction methods such as linear back projection and pseudoinverse with Tikhonov regularization. This evaluation was based on pixel-to-pixel metrics such as root-mean-square error, structural similarity index, 2D correlation coefficient, and peak signal-to-noise ratio. Additionally, the diagnostic value measured by the ROC AUC metric was used to assess the image quality. The results showed that obtaining information about regional lung function (regions affected by pneumothorax or pleural effusion) is possible using image reconstruction based on supervised learning and deep neural networks in EIT. The results obtained using cGAN are strongly better than those obtained using a fully connected network, especially in the case of noisy measurement data. However, diagnostic value estimation showed that even algebraic methods allow us to obtain satisfactory results.