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dc.contributor.supervisor LoVetri, Joe (Electrical and Computer Engineering) en_US
dc.contributor.author Mojabi, Pedram
dc.date.accessioned 2019-03-29T18:19:13Z
dc.date.available 2019-03-29T18:19:13Z
dc.date.issued 2019-05 en_US
dc.date.submitted 2019-03-26T19:34:22Z en
dc.identifier.citation Pedram Mojabi and Joe LoVetri, ``Experimental Evaluation of Composite Tissue-Type Ultrasound and Microwave Imaging," Accepted in IEEE Journal on Multiscale and Multiphysics Computational Techniques, 2019. en_US
dc.identifier.citation Pedram Mojabi and Joe LoVetri, ``Evaluation of Balanced Ultrasound Breast Imaging Under Three Density Assumptions," IEEE Transactions on Computational Imaging, vol. 3, no 4, pp. 864-875, 2017. en_US
dc.identifier.citation Pedram Mojabi and Joe LoVetri, ``Composite Tissue-Type and Probability Image for Ultrasound and Microwave Tomography," IEEE Journal on Multiscale and Multiphysics Computational Techniques, vol. 1, pp. 25-36, 2016. en_US
dc.identifier.citation Pedram Mojabi and Joe LoVetri, ``Ultrasound Tomography for Simultaneous Reconstruction of Acoustic Density, Attenuation, and Compressibility Profiles," Journal of Acoustical Society of America, vol. 137, no. 4, pp. en_US
dc.identifier.citation Pedram Mojabi an Joe LoVetri, ``On the potential use of anatomical and epidemiological information to enhance microwave and ultrasound breast imaging," 2nd URSI AT-RASC, Gran Canaria, Spain, May 2018. en_US
dc.identifier.uri http://hdl.handle.net/1993/33798
dc.description.abstract New approaches to enhance ultrasound tomography (UT) and microwave tomography (MWT) as well as their combination are investigated. These nondestructive imaging techniques create five quantitative images of different properties of an object of interest (OI): (i) compressibility, (ii) acoustic attenuation, (iii) density, (iv)~real part of the complex permittivity (related to the dielectric constant), and (v) the imaginary part of the complex permittivity (related to the conductivity and dielectric loss). These images are reconstructed by solving nonlinear ultrasound and electromagnetic inverse scattering problems, or using ray-based methods. The overall objective of this research is to use UT and MWT so as to (1) enhance the achievable image accuracy related to the detection and identification of various tissues, and (2) to provide quantitative levels of confidence in those reconstruction. This is performed by combining the above five quantitative images into one image that is referred to as the composite tissue type image. For example, for the case of breast imaging, this provides an image of the breast whose pixels correspond to different tissue types (namely, fatty, fibroglandular, tumor, skin, or cyst) within the breast. In addition, each pixel of the image is associated with a probability value that determines the level of confidence regarding its corresponding reconstructed tissue type. This approach is important and novel since existing individual UT and MWT algorithms do not provide any indication regarding the level of confidence in their reconstruction. Furthermore, the approach is ``user friendly'' in the sense that one viewing the image for diagnosis of disease (e.g., a physician) does not have to interpret ultrasonic or electromagnetic properties in order to make a diagnosis. Results of creating tissue-type images from various property images are shown for MRI-based numerical phantoms as well as an experimental tissue mimicking phantom and a human forearm. To perform the initial UT property reconstructions a new balancing method is introduced into the Born Iterative Method (BIM) to deal with the wide range of ultrasonic property values of breast tissues. The possibility of including anatomical and epidemiological information to enhance the reconstruction for the UT/MWT breast imaging application are also investigated. en_US
dc.rights info:eu-repo/semantics/openAccess
dc.subject Ultrasound Tomography, Microwave Tomography, Multi-physics Imaging, Composite Tissue-type image en_US
dc.title Ultrasound and microwave tomography reconstruction algorithms: enhancement and new approaches en_US
dc.type info:eu-repo/semantics/doctoralThesis
dc.type doctoral thesis en_US
dc.degree.discipline Electrical and Computer Engineering en_US
dc.contributor.examiningcommittee Bridges, Greg (Electrical and Computer Engineering) Rickey, Daniel (Physics and Astronomy) Moghaddam, Mahta (USC Viterbi School of Engineering, University of Southern California) en_US
dc.degree.level Doctor of Philosophy (Ph.D.) en_US
dc.description.note May 2019 en_US
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