Mixed Materials
Microwave imaging algorithms: Enhancement and new approaches
| Τίτλος: | Microwave imaging algorithms: Enhancement and new approaches |
|---|---|
| Συγγραφείς: | LoVetri, Joe (Electrical and Computer Engineering) Jeffrey, Ian (Electrical and Computer Engineering), Gilmore, Colin (Electrical and Computer Engineering) Kopotun, Kirill (Mathematics) Popovic, Milica (Electrical and Computer Engineering, McGill University), Abdollahi, Nasim |
| Στοιχεία εκδότη: | 2021-06 |
| Τύπος εγγράφου: | Mixed materials |
| Περίληψη: | New algorithms and techniques to enhance microwave imaging (MWI) are proposed and investigated. MWI algorithms create quantitative images of the dielectric properties of the object of interest (OI) from measured scattered electromagnetic fields obtained by interrogating the OI with non-ionizing and low-power microwave radiation in a non-invasive procedure. Developing MWI algorithms is challenging because of the non-linearity and ill-posedness of MWI inverse problems. The ill-posedness is a fundamental complication influenced by insufficient amount of information about the OI, which is limited to the measurements. State of-the-art MWI algorithms solve the nonlinear electromagnetic inverse problems by means of iterative optimization methods that are computationally intensive procedures. The ill-posedness is addressed by means of applying regularization techniques. This dissertation focuses on investigating and developing WI techniques in an attempt to mitigate these challenges and improve the accuracy and resolution of the MWI reconstructions. First, a novel non-iterative algorithm, which is formulated based on the eigenfunction basis of the Helmholtz operator and referred to as Non-Iterative Eigenfunction-based Inversion (NIEI), is introduced. The formulation of the NIEI algorithm implements a novel regularization approach that exploits the fact that contrast source is zero outside the imaging domain and incorporating this information in form of constraints at virtual points or contours. Unlike the conventional imaging algorithms, the NIEI algorithm does not require standard discretization strategies and the unknowns are represented by expanding in terms of whole-domain eigenfunction basis. Hence, the inverse problem becomes recovering the coefficients of unknown expansions rather than finding the dielectric property of discrete elements. Therefore, the number of unknowns in the inverse problem is reduced significantly and this itself contributes to regularizing the |
| Όροι ευρετηρίου: | Quantitative microwave imaging, Ultrasound imaging, Breast imaging, Multimodality breast imaging, Breast cancer detection, Inverse problem, Contrast source inversion, Non-iterative inversion algorithm, Eigenfunction-based algorithm, Structural prior information, Qualitative eigenfunction-based prior information, doctoral thesis |
| Σύνδεσμος: | |
| Διαθεσιμότητα: | Open access content. Open access content |
| Σημείωση: | English |
| Other Numbers: | UAT oai:mspace.lib.umanitoba.ca:1993/35742 Nasim Abdollahi, Ian Jeffrey and Joe LoVetri, “Non-Iterative Eigenfunction-based Inversion (NIEI) Algorithm for 2D Helmholtz Equation,” Progress In Electromagnetic Research, vol. 85, pp. 1-25, 2019 Nasim Abdollahi, Douglas Kurrant, Pedram Mojabi, Muhammad Omer, Elise Fear, Joe LoVetri, “Incorporation of Ultrasonic Prior Information for Improving Quantitative Microwave Imaging of Breast,” IEEE Journal on Multiscale and Multiphysics Computational Techniques, vol. 4, pp. 98-110, 2019 Nasim Abdollahi, Ian Jeffrey and Joe LoVetri, “Improved Tumor Detection via Quantitative Microwave Breast Imaging Using Eigenfunction-based Prior,” IEEE Transaction on Computational Imaging, vol. 6, pp. 1194-1201, 2020 Nasim Abdollahi, Ian Jeffrey, and Joe LoVetri, “Regularization approaches for a non-iterative eigenfunction-based electromagnetic inversion algorithm,” in 18th International Symposium on Antenna Technology and Applied Electromagnetics (ANTEM), pp. 1–2, IEEE, 2018 1269484756 |
| Πηγή συνεισφοράς: | UNIV OF MANITOBA From OAIster®, provided by the OCLC Cooperative. |
| Αριθμός Καταχώρησης: | edsoai.on1269484756 |
| Βάση Δεδομένων: | OAIster |
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