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Efficient Microwave Imaging Algorithms with On-Body Sensors for Real-Time Biomedical Detection and Monitoring

Islam, Md Asiful
http://orcid.org/0000-0003-0197-5857
http://rave.ohiolink.edu/etdc/view?acc_num=osu1502906869993589

Abstract Details

2017, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Magnetic resonance imaging (MRI), X-ray computed tomography (X-ray CT) etc. are high accuracy imaging modalities but lack portability and cost effectiveness. In contrast, microwave tomography has the potential for real-time portable imaging due to its simpler hardware and lower costs. However, conventional microwave tomography has a number of limitations: a) lengthy times to obtain an image and b) accuracy imbalance between the reconstructed real and imaginary permittivity images, and c) inherent ill-posedness. Further, on-body microwave imaging suffers from several additional challenges. Two of them are, a) lack of reference measurements needed to calibrate the data, b) uncertainty in the positions of the on-body antenna sensors. In this dissertation, we address several of the challenges as related to microwave imaging: 1) we introduce a modified Gauss-Newton algorithm to accelerate image reconstruction (more than 25 times faster than the existing methods) and therefore enable real-time monitoring, 2) to balance real and imaginary permittivity images, we introduce a permittivity reconstruction process that relies on a combination of preset permittivity values. For the first time, this algorithm mitigates, almost entirely, the imbalance of the real and imaginary permittivities, even for lossy biological media. The dissertation also introduces a body-worn monitoring algorithm that images the cross-section of a human torso. To do so, we propose artificial neural networks (ANNs) to establish the unknown relationship between permittivity and measured RF signals. A three-step `self-calibration’ method has been proposed to overcome the unavailability of the reference data. Also, Discrete Fourier Transform (DFT) is employed to reduce the pixel dimensionality without compromising image resolution. Finally, the dissertation is concluded by introducing a novel imaging method without the need for matrix-inversion. This algorithm exploits `reciprocity’ and is among the first demonstrations of the frequency domain analog of the well-known time reversal (TR) algorithm of time domain microwave imaging. Overall, this 3D imaging method is shown to be more robust against data error (works for SNR as low as -5dB), making it suitable for body-worn microwave imaging. Throughout the dissertation, imaging results with synthetic and experimental data are provided to validate the methods and algorithms.
John L. Volakis (Advisor)
Teixeira Fernando (Committee Member)
Kiourti Asimina (Committee Member)
118 p.
Biomedical Engineering; Electrical Engineering; Electromagnetism; Engineering; Medical Imaging

Recommended Citations

Citations

  • Islam, M. A. (2017). Efficient Microwave Imaging Algorithms with On-Body Sensors for Real-Time Biomedical Detection and Monitoring [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1502906869993589

    APA Style (7th edition)

  • Islam, Md Asiful. Efficient Microwave Imaging Algorithms with On-Body Sensors for Real-Time Biomedical Detection and Monitoring. 2017. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1502906869993589.

    MLA Style (8th edition)

  • Islam, Md Asiful. "Efficient Microwave Imaging Algorithms with On-Body Sensors for Real-Time Biomedical Detection and Monitoring." Doctoral dissertation, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1502906869993589

    Chicago Manual of Style (17th edition)

osu1502906869993589
426
© 2017, some rights reserved.Creative Commons License Efficient Microwave Imaging Algorithms with On-Body Sensors for Real-Time Biomedical Detection and Monitoring by Md Asiful Islam is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.