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.

This study investigated the potential of machine-learning-based stroke image reconstruction in capacitively coupled electrical impedance tomography. The quality of brain images reconstructed using the adversarial neural network (cGAN) was examined. The big data required for supervised network training were generated using a two-dimensional numerical simulation. The phantom of an axial cross-section of the head without and with impact lesions was an average of a three-centimeter-thick layer corresponding to the height of the sensing electrodes. Stroke was modeled using regions with characteristic electrical parameters for tissues with reduced perfusion. The head phantom included skin, skull bone, white matter, gray matter, and cerebrospinal fluid. The coupling capacitance was taken into account in the 16-electrode capacitive sensor model. A dedicated ECTsim toolkit for Matlab was used to solve the forward problem and simulate measurements. A conditional generative adversarial network (cGAN) was trained using a numerically generated dataset containing samples corresponding to healthy patients and patients affected by either hemorrhagic or ischemic stroke. The validation showed that the quality of images obtained using supervised learning and cGAN was promising. It is possible to visually distinguish when the image corresponds to the patient affected by stroke, and changes caused by hemorrhagic stroke are the most visible. The continuation of work towards image reconstruction for measurements of physical phantoms is justified.

Diffractive optical elements (DOE) play an important role for laser beam shaping in industry, for example in lithography or parallel laser material processing. Typically such applications require high damage threshold and low background illumination (high contrast and efficiency). Usual DOE with binary phase (step-like) profiles are made microlithographically and suffer from substantial scattering on profile derivative discontinuities. That gives also tendency to lower damage threshold as compared to intrinsic material values. The LIMO approach is based contrarily on a proprietary, non etching material processing and is suitable for manufacturing of high-precision free programmable continuous surface profiles in optical glasses and crystals. We report on linear symmetric diffractive beam splitter 1:11 with high homogeneity and efficiency > 95% and discuss also other DOE designs. The design data, simulations with measured surface profiles and experimental intensity distributions are in very good agreement. Furthermore we report on a new type of optical attenuator composed from two DOE gratings. Its dynamic transmission range is 0.3% to 98%. The required lateral DOE shift is only 5 - 10 μm in the present design, so that the device can be very fast and applicable for dynamic intensity stabilization.

This study investigated the potential of the generative adversarial neural network (cGAN) image reconstruction in industrial electrical capacitance tomography. The image reconstruction quality was examined using image patterns typical for a two-phase flow. The training dataset was prepared by generating images of random test objects and simulating the corresponding capacitance measurements. Numerical simulations were performed using the ECTsim toolkit for MATLAB. A cylindrical sixteen-electrode ECT sensor was used in the experiments. Real measurements were obtained using the EVT4 data acquisition system. The reconstructed images were evaluated using selected image quality metrics. The results obtained using cGAN are better than those obtained using the Landweber iteration and simplified Levenberg–Marquardt algorithm. The suggested method offers a promising solution for a fast reconstruction algorithm suitable for real-time monitoring and the control of a two-phase flow using ECT.

The aim of the article was to analyze the potential simultaneous use of magnetic nanoparticles as contrast agents in MRI imaging and for magnetic hyperthermia. The study proposed characterizing the nanoparticles using various measurement methods in order to investigate the relationships between different properties. The first stage involved measuring images of nanoparticle samples using scanning transmission electron microscopy (TEM) and dynamic light scattering (DLS). The diameter distribution of nanoparticles was determined based on image segmentation. The next step involved measuring relaxation properties of nanoparticles in low and high magnetic fields. The research was carried out for nanoparticle solutions of various concentrations and properties. The last step was measuring calorimetric properties of nanoparticles as a thermal source under alternating magnetic field excitation conditions. The range of nanoparticle diameters (20–25 nm) for which maximum losses occur in an alternating magnetic field corresponds to the diameter range in which the maximum r₂ relaxivity is observed.