Import, process and visualize Sciospec EIT data in the open-source software EIDORS by taking the example of a standard phantom experiment
From research targeted instruments like the EIT16 to fully customized OEM products for bioanalytical, medical and industrial applications Sciospec provides highly specialized solutions for electrical impedance tomography. Flexible channel configurations, frequency sweep modes, scalability up to several hundred channels and broad options for extension through sensor adapters, add-on modules and more make Sciospec EIT systems suitable for a multitude of ambitious applications.
EIDORS (Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software) is a free software for EIT image reconstruction and processing. EIDORS is based on MATLAB, but also works with the free open source alternative Octave and is broadly used and supported by the academic community and EIT experts worldwide. The Sciospec EIT16 – 16 channel EIT device – is a full featured high bandwidth EIT system for ambitious research applications. The EIT data generated using a Sciospec EIT system, like the EIT16, and corresponding Sciospec software can be imported, processed and visualized in the Matlab based free software EIDORS. Sciospec provides appropriate Matlab code examples and guidance to help you getting started with this constellation. This Application Note demonstrates how to import and perform EIT image reconstruction in EIDORS using the Matlab code provided by Sciospec. Some sample data were generated in a standard phantom experiment using the Sciospec EIT16 and corresponding Sciospec software.
Electrical impedance tomography (EIT) provides functional images of an electrical conductivity distribution inside the human body. Since the 1980s, many potential clinical applications have arisen using inexpensive portable EIT devices. EIT acquires multiple trans-impedance measurements across the body from an array of surface electrodes around a chosen imaging slice. The conductivity image reconstruction from the measured data is a fundamentally ill-posed inverse problem notoriously vulnerable to measurement noise and artifacts. Most available methods invert the ill-conditioned sensitivity or the Jacobian matrix using a regularized least-squares data-fitting technique. Their performances rely on the regularization parameter, which controls the trade-off between fidelity and robustness. For clinical applications of EIT, it would be desirable to develop a method achieving consistent performance over various uncertain data, regardless of the choice of the regularization parameter. Based on the analysis of the structure of the Jacobian matrix, we propose a fidelity-embedded regularization (FER) method and a motion artifact reduction filter. Incorporating the Jacobian matrix in the regularization process, the new FER method with the motion artifact reduction filter offers stable reconstructions of high-fidelity images from noisy data by taking a very large regularization parameter value. The proposed method showed practical merits in experimental studies of chest EIT imaging.
A tubular electrical impedance tomograph (EIT) with micrometric dimensions was fabricated by using rolledup nanotechnology. This approach gives access to EIT devices with tunable sizes in the sub-100 µm range. EIT images of silicon dioxide microparticles were obtained as proof of principle. These devices could enable the impedimetric analysis of biological micro-scale objects, such as single cells or small cell clusters.
In recent years, there has been a growing interest in the analysis of single cells. Single-cell studies give valuable insights in the variability of biological cells in one and the same cell population, and can give new information about their fundamental properties. Impedance measurements are especially well-suited for these analyses, as they are labelfree and non-destructive. Tomographic measurements in particular are of interest since they can additionally provide spatial information in real time. EIT studies of single cells call for appropriate measurement chambers, whose sizes should be similar to that of the object to be studied. This can easily be achieved using rolled-up nanotechnology.
We reconstructed conductivity changes of human lungs caused by breathing. “The Principal Component Analysis Based Local Reconstruction Method” was applied for lung EIT. Own Matlab GUI was developed which loads .txt data file exported from the Sciospec EIT16 device.
The main idea was to extract lung data from the measured data and to find the corresponding lung region. Low-pass filter and PCA were used which can be considered as machine learning techniques. The standard sensitivity method with Tikhonov regularization was used for reconstruction on the local region with the extracted data
This paper proposes a mathematical model of a pressure-sensitive conductive fabric sensor, which adopts the technique of electrical impedance tomography (EIT) with a composite fabric being capable of changing its effective electrical property due to an applied pressure. We model the composite fabric from an electrically conductive yarn and a sponge-like non-conductive fabric with high pore density, and the conductive yarn is woven in a wavy pattern to possess a pressure-sensitive conductive property, in the sense of homogenization theory. We use a simplified version of EIT technique to image the pressure distribution associated with the conductivity perturbation. A mathematical ground for the effective conductivity in one-direction is provided. We conduct an experiment to test the feasibility of the proposed pressure sensor.
The goal is to discriminate admittivity anomaly from background which consists of high-contrast materials. To remove the influence of the inhomogeneous background, linear combinations of multi-frequency EIT (mfEIT) data are used to produce contrast images.
Multiple backgrounds subtraction method is used for the linear combinations of mfEIT data.
Time-difference EIT(tdEIT) is to recover the time change of conductivity distribution using the time change of voltage data.
User made java GUI can control Sciospec EIT device to perform tdEIT experiments.
To perform tdEIT, we put insulating glass in the circular saline tank. The reference voltage data is measured in the absence of the glass. The author made reconstruction algorithm is used to locate the position of the glass.
Pulmonary activity can be visualized with Sciospec 16-channel EIT device by using time-difference imaging technique.
Software can easily control Sciospec device to perform real-time image reconstruction. The following images are of the software made by C#.
FER algorithm is used for the image reconstruction. See the paper ‘A fidelity-embedded regularization method for robust electrical impedance tomography’ for details.