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Image reconstruction theory and implementation for low-dose X-ray computed tomography

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dc.contributor.advisor Liang, Jerome Z en_US
dc.contributor.advisor Subbarao, Muralidhara en_US
dc.contributor.author Liu, Yan en_US
dc.contributor.other Department of Electrical Engineering en_US
dc.date.accessioned 2017-09-20T16:52:46Z
dc.date.available 2017-09-20T16:52:46Z
dc.date.issued 2014-12-01 en_US
dc.identifier.uri http://hdl.handle.net/11401/77476 en_US
dc.description 148 pgs en_US
dc.description.abstract The excessive X-ray radiation exposure during clinical examinations has been reported to be linked to increase lifetime risk of cancers in patients. Directly lower computed tomography (CT) dose without improving reconstruction technique will degrade the image quality and is not acceptable. The objective of this dissertation is investigating novel reconstruction methods to improve image quality in low-dose cases. In practice, it is usually more convenient to improve the conventional analytical methods by refining projection model and designing new filters due to the fast computing time and low computational complexity. However, the reconstructions from analytical methods are still sensitive to artifacts and photon noise; therefore, the improved analytical methods may not be applicable to low-dose CT reconstructions. Recently, iterative image reconstruction methods have been found to be very effective in low-dose CT reconstruction and can be mainly classified into two categories: statistical iterative reconstruction methods and algebraic iterative reconstruction methods. The statistical iterative reconstruction methods, which incorporate statistical noise model, prior model and projection geometry, have shown the ability to reduce noise and improve resolution for image reconstruction from low-mAs projection data. The algebraic iterative reconstruction methods, which were originally invented in 1970s, have been improved in the past decade to reconstruct image from sparse-view projection data, particularly when adequate prior models are used as objective functions. In this dissertation, four improved reconstruction methods are proposed and discussed for different types of low-dose data (for example: low-mAs and sparse-view data). Both computer simulation and real data (i.e., physical phantom and patients' data) are used for evaluations. The clinical potentials of the proposed methods are also exploited in this dissertation. en_US
dc.description.sponsorship This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. en_US
dc.format Monograph en_US
dc.format.medium Electronic Resource en_US
dc.language.iso en_US en_US
dc.publisher The Graduate School, Stony Brook University: Stony Brook, NY. en_US
dc.subject.lcsh Electrical engineering en_US
dc.subject.other analytical reconstruction, Computed Tomography, image reconstruction, iterative reconstruction, total variation stokes, volume shadow weighting en_US
dc.title Image reconstruction theory and implementation for low-dose X-ray computed tomography en_US
dc.type Dissertation en_US
dc.mimetype Application/PDF en_US
dc.contributor.committeemember Robertazzi, Thomas en_US
dc.contributor.committeemember Gindi, Gene en_US
dc.contributor.committeemember Moore, William en_US


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