Modeling for missing tissue compensator fabrication using RFID tag in U-Health

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Abstract

U-Health (Ubiquitous based Healthcare System that supports medical services) is one of the technology areas proposed to realize the vision of ubiquitous computing. A plethora of different alternative or complementary RFID sensing technologies and RFID management systems are available. And mostly RFID technologies in medical facilities are applied for tracking a patient's location, storing medical equipment, and keeping on patient's record. In this paper, we primarily apply RFID technology to measure the affected part which is needed to gain information about its volume, size and mass. Thus we will propose modeling method with using RFID tags for making missing tissue compensator which is used in Radiation Therapy. The missing tissue compensator is commonly used to maximize the effect of skin protection and to irradiate an even dose on tumor tissue. Existing missing tissue compensator marked the contour of the body surface directly on the patient's skin using a curved ruler or used medical images such as computerized tomography images and magnetic resonance images. In addition, the application of medical images is expensive. In this paper we will obtain necessary 3 dimension location information using RFID technology which is fixed on the surface of the patient's affected parts using a rubber mask. The rubber mask has RFID tags on its surface. So RFID readers to detect RFID tags on the mask obtain each of tags' location information, and we calculate them to make a missing tissue compensator. According to the result, the missing tissue compensator modeled in this research compensated defective tissue and protected normal tissue, so it was considered clinically applicable. © Springer-Verlag Berlin Heidelberg 2006.

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Choi, O. H., Lim, J. E., Na, H. S., & Baik, D. K. (2006). Modeling for missing tissue compensator fabrication using RFID tag in U-Health. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4345 LNBI, pp. 463–471). Springer Verlag. https://doi.org/10.1007/11946465_42

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