It is well known that a finite-size scatterer has a set of natural resonances, which are uniquely determined by the physical properties of the scatterer. This is also the case for a breast tumor which can be regarded as a dielectric scatterer. Since the scatterer is naturally "tuned" at the resonances, it is expected that an increased electromagnetic coupling would take place at the resonance frequencies compared to other frequencies. For a breast tumor, this would mean a higher power absorption, indicating a faster temperature increase resulting in more efficient hyperthermia.
In this dissertation, an adaptive microwave concept is demonstrated for breast cancer applications. The general approach is to detect and identify the tumor-specific resonance, determine the electrical location of the tumor, and apply the focused microwave hyperthermia using the identified resonance and the electrical location. The natural resonances vary depending on the tumor size, shape, and breast tissue configuration. Therefore, an adaptive tuning of the microwave source to tumor-specific resonance frequencies could improve the overall efficiency of hyperthermia treatment by allowing for a faster and more effective heating to achieve a desired therapeutic temperature level.
Applying the singularity expansion method (SEM), both the resonances and the electrical location can be obtained from the poles and residues, respectively. This SEM-based approach is computationally inexpensive and can easily be implemented as a combination processing into emerging UWB microwave systems. Alternatively, a relatively simple microwave system based on this concept can potentially be used in conjunction with existing mammography. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/29096 |
| Date | 30 October 2012 |
| Creators | Hong, Sun |
| Contributors | Electrical and Computer Engineering, Davis, William A., Stutzman, Warren L., Safaai-Jazi, Ahmad, Pratt, Timothy J., Adjerid, Slimane |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | Hong_SK_D_2012.pdf |
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