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http://hdl.handle.net/11375/27533
Title: | ADVANCES IN REAL-TIME QUANTITATIVE NEAR-FIELD MICROWAVE IMAGING FOR BREAST CANCER DETECTION |
Other Titles: | QUANTITATIVE MICROWAVE IMAGING FOR BREAST CANCER DETECTION |
Authors: | Daniel, Tajik |
Advisor: | Nikolova, Natalia K. Bandler, John W. |
Department: | Electrical and Computer Engineering |
Keywords: | holography;breast cancer;imaging;microwave;quantitative microwave holography;phantom;Born;Rytov;filtering;inverse problems;near field;range migration;synthetic aperture radar (SAR) |
Publication Date: | 2022 |
Abstract: | Microwave imaging finds numerous applications involving optically obscured targets. One particular area is breast cancer detection, since microwave technology promises fast low-cost image reconstruction without the use of harmful radiation typical of X-ray mammography. However, the success of microwave imaging is hindered by a critical issue, the complex nature of near-field electromagnetic scattering in tissue. To overcome this, specialized image reconstruction algorithms alongside sensitive measurement hardware are required. In this work, real-time near-field microwave imaging algorithms known as quantitative microwave holography and scattered power mapping are explored. They are experimentally demonstrated to identify potential tumor regions in tissue phantoms. Alongside this development, quality control techniques for evaluating microwave hardware are also described. Two new methods for improving the image reconstruction quality are also presented. First, a novel technique, which combines two commonly used mathematical approximations of scattering (the Born and Rytov approximations), is demonstrated yielding improved image reconstructions due to the complimentary nature of the approximations. Second, a range migration algorithm is introduced which enables near-field refocusing of a point-spread function (PSF), which is critical for algorithms that rely on measured PSFs to perform image reconstruction. |
URI: | http://hdl.handle.net/11375/27533 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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tajik_daniel_2022april_phd.pdf | Daniel Tajik PhD Thesis | 20.53 MB | Adobe PDF | View/Open |
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