Wykaz obszarów badawczych związanych z tagiem Image-reconstruction:
| # | Obszar badawczy | Dziedzina naukowa |
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| 1 |
“Capacitively coupled impedance tomography for anatomical and functional imaging” To date, electrical impedance tomography (EIT) using sinusoidal excitation has been regarded as the most promising electrical imaging technique for diagnostic medical applications. However, the high impedance at the electrode-skin contact remains a significant challenge, hindering both the development and practical implementation of this technology. To address these limitations, an alternative approach utilizing non-contact electrodes and pulse excitation will be explored. This method will involve signal shape analysis to determine both components of admittance. Such data will enable the reconstruction of images depicting electrical permittivity and conductivity. Capacitively coupled electrical tomography will be investigated using numerical and physical lung phantoms, with a focus on regional ventilation distribution. Key metrics such as measurement sensitivity, contrast, and spatial-temporal resolution of the resulting images will be evaluated. Non-linear iterative algorithms and deep learning techniques will be employed for image reconstruction. Real measurements will be conducted using a simplified thorax phantom designed to simulate the respiratory cycle. A prototype flexible sensor incorporating surface electrodes will be developed, along with a mechanical-electrical lung phantom. Measurements will be carried out using the 32-channel electrical capacitance tomograph EVT4, which was designed and constructed at ZEJiM.
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| 2 |
High-Spatial Resolution Electrical Capacitance Tomography Electrical capacitance tomography (ECT) is a non-invasive imaging technique that reconstructs the spatial distribution of electrical permittivity from capacitance measurements at the interface of the object being examined. Since its introduction, ECT has been extensively studied, particularly in industrial process monitoring, multiphase flow imaging, and process tomography. Current research focuses on improving image quality through sensor and front-end electronics design, iterative model-based algorithms, regularization, statistical methods, and, more recently, machine learning methods. The image spatial resolution of ECT remains fundamentally limited by the small number and relatively large size of sensing electrodes, the non-uniform spatial sensitivity distribution, and the nonlinear nature of the inverse problem. Increasing the number of electrodes can provide additional measurement information, but it also poses significant challenges related to measurement accuracy, signal-to-noise ratio, and system complexity. Consequently, most state-of-the-art electrical capacitance tomography systems still use a small number of electrodes and provide relatively low-resolution images. Recent research is increasingly exploring new sensor geometries, optimized electrode configurations, advanced capacitance measurement electronics, and data-driven reconstruction methods. However, it has not yet been possible to demonstrate that increasing the nominal number of sensing elements effectively improves spatial resolution. The proposed research addresses this gap by examining electrodes synthesized from densely arranged electrode segments, advanced measurement strategies, numerical modeling, and dedicated reconstruction methods. A combined numerical and experimental approach will be used to determine the physical and technological limitations of high-spatial-resolution ECT and to develop design principles for next-generation ECT sensors.
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