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dc.contributor.authorBoroomand, Ameneh 20:47:55 (GMT) 20:47:55 (GMT)
dc.description.abstractThe quality of medical imaging is an important factor that can directly affect on the results of different medical imaging tests which routinely being used for various clinical studies and related research. The existence of any type of image imperfection such as different noise and artifacts can make some difficulties for the correct analyzing and consequent interpretation of medical imaging test results and even may impact on the overall decision of the performed clinical procedure. Due to this issue, there is always a motivation for the quality enhancement of medical imaging that needs to be done by correcting for the different image degradation that typically arise during medical image capture or reconstruction. Computational compensated imaging provides a useful, cheap and easy solution for the quality enhancement of different types of medical imaging when it compares to the similar hardware-based compensation methods. A computational compensated medical imaging basically aims to recover a compensated (true) image from degraded medical measurement that is affected by the different types of acquisition/reconstruction image degradation. Compensating for each single image degradation issue can be useful for the quality improvement of medical imaging. Having a computational compensated imaging framework to jointly correct for multiple acquisition/reconstruction degradation issues can improve the functionality of current existing compensated imaging frameworks as they mostly account only for a single specific type of degradation issue. This work presents a novel probabilistic based computational compensated medical imaging which is able to jointly account for several acquisition/reconstruction degradation issues from the different domains in a unified computational framework. The developed computational cross-domain compensated medical imaging specifically takes advantage of a stochastically fully connected conditional random field (SFC-CRF) model in its frame- work which improves the performance of the proposed compensated medical imaging in producing of a desired compensated medical image. A compensated optical coherence tomography (C-OCT) imaging is developed within the framework of proposed computational cross-domain compensated medical imaging and with aiming to jointly compensate for the degradation due to the optical aberrations and speckle noise in OCT imaging. The developed C-OCT imaging is expanded to design a compensated super resolution OCT (C-SR-OCT) imaging framework which is able to generate a super resolution OCT (SR-OCT) image of higher quality from multiple OCT measurements. The proposed computational cross-domain compensated medical imaging is also used to develop a compensated magnetic resonance imaging (CMR) framework which aims to improve the quality of MR imaging from different modalities by jointly correcting for the MR image degradation due to the intrinsic properties of MR scanner, bias field inhomogeneities and inherent MR noise. The results of all three designed compensated medical imaging platforms for the OCT imaging and MR imaging elaborate the promising efficacy of proposed probabilistic based computational compensated medical imaging framework for the quality enhancement of different medical imaging techniques.en
dc.publisherUniversity of Waterlooen
dc.subjectMedical image processingen
dc.subjectMedical image quality improvementen
dc.subjectProbabilistic modelingen
dc.titleA Unified Probabilistic Computational Framework for Cross-Domain Compensated Medical Imagingen
dc.typeDoctoral Thesisen
dc.pendingfalse Design Engineeringen Design Engineeringen of Waterlooen
uws-etd.degreeDoctor of Philosophyen
uws.contributor.advisorWong, Alexander
uws.contributor.advisorBizheva, Kostadinka
uws.contributor.affiliation1Faculty of Engineeringen

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