Feasibility Study of Detection of Coronavirus Disease 2019 with Microwave Medical Imaging
| Autor: | Yifan Chen, Pedro Valdes Sosa, Mitchel Joseph Valdes Sosa, Zheng Gong, Yahui Ding, Xiaoyou Lin |
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| Rok vydání: | 2021 |
| Předmět: |
Thorax
Permittivity Pixel Computer science business.industry 020208 electrical & electronic engineering 020206 networking & telecommunications Pattern recognition 02 engineering and technology Imaging phantom respiratory tract diseases body regions Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Medical imaging A priori and a posteriori Artificial intelligence business Microwave |
| Zdroj: | 2021 15th European Conference on Antennas and Propagation (EuCAP). |
| DOI: | 10.23919/eucap51087.2021.9411374 |
| Popis: | This paper studies the feasibility of detecting the pneumonia due to the COVID-19 with microwave medical imaging. One challenge while formulating such a problem is to identify the disease in lungs whose dielectric permittivity is dynamically fluctuating with the respiration. In this paper, we utilize this feature by assuming that the permittivity of the disease has minor variation at microwave frequencies during the respiration, and thus the dielectric variance of the pixels at the diseased site over a number of consecutive images significantly differs from those of the other tissues in the thorax. Based on this assumption, we propose two approaches that make use of the a priori information (API) on the position of the heart and the symmetry of the thorax, respectively, to identify a diseased lung. Finally, these two approaches are numerically validated on a thorax phantom, and their performance is compared. |
| Databáze: | OpenAIRE |
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