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Image reconstruction theory and impl...

Liu, Yan.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Image reconstruction theory and implementation for low-dose X-ray computed tomography./
作者:	Liu, Yan.
面頁冊數:	148 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.
Contained By:	Dissertation Abstracts International76-07B(E).
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3683178
ISBN:	9781321571998

Image reconstruction theory and implementation for low-dose X-ray computed tomography.
Liu, Yan.

Image reconstruction theory and implementation for low-dose X-ray computed tomography. - 148 p.

Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2014.

This item must not be sold to any third party vendors.

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.

ISBN: 9781321571998Subjects--Topical Terms:

649834
Electrical engineering.

Image reconstruction theory and implementation for low-dose X-ray computed tomography.
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520 $a 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.
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