عنوان

Efficient Microwave Imaging Algorithms with On-body Sensors for Real-time Biomedical Detection and Monitoring

شماره

TL50007

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Efficient Microwave Imaging Algorithms with On-body Sensors for Real-time Biomedical Detection and Monitoring

نام عام مواد

[Thesis]

نام نخستين پديدآور

Asiful Islam

نام ساير پديدآوران

Volakis, John L.

نام ناشر، پخش کننده و غيره

The Ohio State University

تاریخ نشرو بخش و غیره

2017

نام خاص و کميت اثر

119

متن يادداشت

Committee members: Asimina, Kiourti; Fernando, Teixeira

متن يادداشت

Place of publication: United States, Ann Arbor; ISBN=978-0-438-81387-8

جزئيات پايان نامه و نوع درجه آن

Ph.D.

نظم درجات

Electrical and Computer Engineering

کسي که مدرک را اعطا کرده

The Ohio State University

امتياز متن

2017

متن يادداشت

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.

موضوع مستند نشده

Engineering; Biomedical engineering; Electrical engineering; Medical imaging

اصطلاح موضوعی

Applied sciences;Health and environmental sciences;Gauss-Newton algorithm;Magnetic resonance imaging;Microwave tomography

مستند نام اشخاص تاييد نشده

Beshara, Robert K.

مستند نام اشخاص تاييد نشده

Volakis, John L.

تقسيم فرعي

Electrical and Computer Engineering

مستند نام تنالگان تاييد نشده

The Ohio State University

شماره بازیابی

2184260247; 13834476

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y