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" Efficient Microwave Imaging Algorithms with On-body Sensors for Real-time Biomedical Detection and Monitoring "
Asiful Islam
Volakis, John L.
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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805158
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| Doc. No
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TL50007
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| Call number
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2184260247; 13834476
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| Main Entry
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Beshara, Robert K.
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| Title & Author
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Efficient Microwave Imaging Algorithms with On-body Sensors for Real-time Biomedical Detection and Monitoring\ Asiful IslamVolakis, John L.
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| College
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The Ohio State University
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| Date
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2017
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| Degree
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Ph.D.
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| field of study
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Electrical and Computer Engineering
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| student score
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2017
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| Page No
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119
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| Note
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Committee members: Asimina, Kiourti; Fernando, Teixeira
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| Note
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Place of publication: United States, Ann Arbor; ISBN=978-0-438-81387-8
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| Abstract
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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.
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| Subject
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Engineering; Biomedical engineering; Electrical engineering; Medical imaging
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| Descriptor
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Applied sciences;Health and environmental sciences;Gauss-Newton algorithm;Magnetic resonance imaging;Microwave tomography
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| Added Entry
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Volakis, John L.
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| Added Entry
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Electrical and Computer EngineeringThe Ohio State University
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