• 유체기계공업학회:학술대회논문집
• /
• 2006.08a
• /
• pp.475-478
• /
• 2006

In electrical impedance tomography (EIT) an array of electrodes is attached on the boundary of an object and small alternating currents are injected through these electrodes, and then the resulting voltages are measured. An estimation for the cross-sectional resistivity distribution in the object is obtained by using these current and voltage data in a nondestructive manner. In this paper, the electrical impedance imaging of two-phase flows undergoing rapid transient is considered with a special emphasis on the effect of the current pattern on the image reconstruction. The trigonometric current pattern, which is commonly used in the conventional static electrical impedance imaging, shows poor performance in case of the dynamic imaging considered in this work. Extensive numerical experiments are conducted with various kinds of current patterns and their effects on the image reconstruction performance are examined.

• 한국지구물리탐사학회:학술대회논문집
• /
• 2007.06a
• /
• pp.1-6
• /
• 2007

Two impedance imaging systems of multi-frequency electrical impedance tomography (MFEIT) and magnetic resonance electrical impedance tomography (MREIT) are described. MFEIT utilizes boundary measurements of current-voltage data at multiple frequencies to reconstruct cross-sectional images of a complex conductivity distribution (${\sigma}+i{\omega}{\varepsilon}$) inside the human body. The inverse problem in MFEIT is ill-posed due to the nonlinearity and low sensitivity between the boundary measurement and the complex conductivity. In MFEIT, we therefore focus on time- and frequency-difference imaging with a low spatial resolution and high temporal resolution. Multi-frequency time- and frequency-difference images in the frequency range of 10 Hz to 500 kHz are presented. In MREIT, we use an MRI scanner to measure an internal distribution of induced magnetic flux density subject to an injection current. This internal information enables us to reconstruct cross-sectional images of an internal conductivity distribution with a high spatial resolution. Conductivity image of a postmortem canine brain is presented and it shows a clear contrast between gray and white matters. Clinical applications for imaging the brain, breast, thorax, abdomen, and others are briefly discussed.

• Journal of Biomedical Engineering Research
• /
• v.25 no.3
• /
• pp.183-188
• /
• 2004

Magnetic resonance current density imaging (MRCDI) is a useful method for measuring electrical current density distribution inside an object. To avoid object rotations during the conventional MRCDI scans, we have reconstructed current density component images by applying a spatial filter to the magnetic field data measured both inside and outside the object. To measure the magnetic field outside the object with MRI, we immersed the object in a water tank. To evaluate accuracy of the current density imaging, we have made a conductivity phantom with a corresponding finite element method model. We have compared the experimentally obtained current density images with the ones calculated by the finite element method. The average errors of the reconstructed current density images were 6.6 ∼ 45.4 ％ when the injected currents were 1 ∼ 24 mA. We expect that the current density component imaging technique can be used in diverse biomedical applications such as electrical therapy system developments and biological electrical safety analysis.

• Progress in Medical Physics
• /
• v.30 no.2
• /
• pp.39-48
• /
• 2019

Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.

• Communications of the Korean Mathematical Society
• /
• v.16 no.3
• /
• pp.519-541
• /
• 2001

Magnetic Resonance Electrical Impedance Tomography(MREIT) is a new medical imaging technique for the cross-sectional conductivity distribution of a human body using both EIT(Electrical Impedance Tomography) and MRI(Magnetic Resonance Imaging) system. MREIT system was designed to enhance EIT imaging system which has inherent low sensitivity of boundary measurements to any changes of internal tissue conductivity values. MREIT utilizes a recent CDI (Current Density Imaging) technique of measuring the internal current density by means of MRI technique. In this paper, a mathematical modeling for MREIT and image reconstruction method called the alternating J-substitution algorithm are presented. Computer simulations show that the alternating J-substitution algorithm provides accurate high-resolution conductivity images.

• Journal of Industrial Technology
• /
• v.27 no.B
• /
• pp.91-96
• /
• 2007

Electrical impedance tomography (EIT) is a technique for determining the electrical conductivity and permittivity distribution within the interior of a body from measurements made on its surface. One recent application area of the EIT is the detection of breast cancer by imaging the conductivity and permittivity distribution inside the breast. The present standard for breast cancer detection is X-ray mammography, and it is desirable that EIT and X-ray mammography use the same geometry. A forward model of a simplified mammography geometry for EIT imaging was proposed earlier. In this paper, we propose an iterative algorithm for computing the current pattern that will be applied to the electrodes. The current pattern applied to the electrodes influences the voltages measured on the electrodes. Since the measured voltage data is going to be used in the impedance imaging computation, it is desirable to apply currents that result in the largest possible voltage signal. We compute the eigenfunctions for a homogenous medium that will be applied as current patterns to the electrodes. The algorithm for the computation of the eigenfunctions is presented. The convergence of the algorithm is shown by computing the eigencurrent of the simplified mammography geometry.

• Progress in Superconductivity and Cryogenics
• /
• v.18 no.2
• /
• pp.1-4
• /
• 2016

To observe the superconducting current and structural properties of high critical temperature ($T_c$) superconductors (HTS), we suggest the following imaging methods: Room temperature imaging (RTI) through thermal heating, low-temperature bolometric microscopy (LTBM) and Raman scattering imaging. RTI and LTBM images visualize thermal-electric voltages as different thermal gradients at room temperature (RT) and superconducting current dissipation at near-$T_c$, respectively. Using RTI, we can obtain structural information about the surface uniformity and positions of impurities. LTBM images show the flux flow in two dimensions as a function of the local critical currents. Raman imaging is transformed from Raman survey spectra in particular areas, and the Raman vibration modes can be combined. Raman imaging can quantify the vibration modes of the areas. Therefore, we demonstrate the spatial transport properties of superconducting materials by combining the results. In addition, this enables visualization of the effect of current flow on the distribution of impurities in a uniform superconducting crystalline material. These imaging methods facilitate direct examination of the local properties of superconducting materials and wires.

• Journal of Sensor Science and Technology
• /
• v.30 no.5
• /
• pp.342-348
• /
• 2021

For the characterization or failure analysis of electronic devices such as PCB (printed circuit boards), the most common method is the measurement of voltage waveforms with an oscilloscope. However, because there are many types of problems that cannot be detected by voltage waveform analysis, several other methods such as X-ray transmission, infrared imaging, or eddy current measurement have been applied for these analyses. However, these methods have also been limited to general analyses because they are partially useful in detecting physical defects, such as disconnections or short circuits. Fundamentally current waveform measurements during the operation of electronic devices need to be performed, however, commercially available current sensors have not yet been developed, particularly for applications in highly integrated PCB products with sub-millimeter fine pitch. In this study, we developed a highly sensitive PHR (planar hall resistance) magnetic sensor for application in highly integrated PCBs. The developed magnetic sensor exhibited sufficient features of an ultra-small size of less than 340 ㎛, magnetic field resolution of 10 nT, and current resolution of 1 mA, which can be applicable for PCB analyses. In this work, we introduce the development process of the magnetic sensing probe and its characteristic results in detail, and aim to extend this pin-type current probe to applications such as current distribution imaging of PCBs.

• Current Optics and Photonics
• /
• v.7 no.5
• /
• pp.537-544
• /
• 2023

Differential phase contrast (DPC) microscopy, a central quantitative phase imaging (QPI) technique in cell biology, facilitates label-free, real-time monitoring of intrinsic optical phase variations in biological samples. The existing DPC imaging theory, while important for QPI, is grounded in paraxial diffraction theory. However, this theory lacks accuracy when applied to high numerical aperture (NA) systems that are vital for high-resolution cellular studies. To tackle this limitation, we have, for the first time, formulated a nonparaxial DPC imaging equation with a transmission cross-coefficient (TCC) for high NA DPC microscopy. Our theoretical framework incorporates the apodization of the high NA objective lens, nonparaxial light propagation, and the angular distribution of source intensity or detector sensitivity. Thus, our TCC model deviates significantly from traditional paraxial TCCs, influenced by both NA and the angular variation of illumination or detection. Our nonparaxial imaging theory could enhance phase retrieval accuracy in QPI based on high NA DPC imaging.

• Journal of Biomedical Engineering Research
• /
• v.28 no.2
• /
• pp.265-270
• /
• 2007

Polarization imaging systems have been widely used to selectively characterize skin lesions. Nevertheless, current systems are used in single-mode due to the limitations of a fixed polarization mode and a single-working distance of light source, in which uniform light distribution is achieved on target area. To address such limitations, we developed a variable polarization imaging system based on multi-working distance of light source for various clinical diagnoses. In this study, we characterize the imaging system and present experiment results demonstrating its clinical usefulness. The imaging system consists of a CCD color camera, linear polarization filters, and a single-layered LED ring light source which provides uniform light distribution at multi-working distances. The first polarizer was placed on the light source and the second polarizer placed on objective lens provides continuous linear polarization angle from $0^{\circ}\;to\;90^{\circ}$. The clinical efficacy of the imaging system was investigated by acquiring and analyzing clinical images of skin wrinkle and dental plaque. With the experiments, we verified the potential usefulness of the imaging system for other clinical applications.