| Notes: | Advisor: Xiaochuan Pan.
Thesis (Ph.D.)--The University of Chicago, Division of the Biological Sciences, and The Pritzker School of Medicine, Committee on Medical Physics, 2013.
Dissertation Abstracts International, Volume: 74-11(E), Section: B.
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| Summary: | X-ray computed tomography (CT), since its advent, has become an important clinical imaging tool for providing three-dimensional information of the internal structure of imaged subjects. In recent years, cone-beam CT (CBCT), an emerging technology based upon CT, has experienced remarkable growth and is quickly entering the clinical environment. It has enabled a wide range of applications for fulfilling clinical and pre-clinical needs particularly in image-guided radiation therapy (IGRT), image-guided surgery (IGS), and micro-CT imaging.
Current CBCT systems employ traditional, analytic-based algorithms such as filtered backprojection (FBP) for image reconstruction. These algorithms inherently set stringent requirements on data acquisition, such as scan geometry and sampling density, which may not be satisfied by current CBCT systems. Furthermore, considerable limitations are posed to flexible design of innovative CBCT scans and systems possessing desired properties, such as lowered imaging dose. On the other hand, optimization-based algorithms, owing to the accurate incorporation of imaging model, the flexible imposition of image- and data-based constraints, and the ample availability of iterative-algorithmic tools, can potentially provide a viable solution to overcoming limitations of analytic-based algorithms.
In this dissertation, we conducted an investigation of optimization-based algorithms for use in CBCT image reconstruction. Numerical experiments were performed for optimization-based algorithms, by use of simulated, ideal data, for verifying their capability of solving the reconstruction programs. We then adapted the optimization-based algorithms for reconstructing CBCT images under practical imaging conditions using real phantom, patient, and specimen data. We characterized optimization-based algorithms and assessed their utility specifically for a number of visualization- and quantitative-measurement-based tasks. The results of the dissertation work show that, optimization-based algorithms can reconstruct CBCT images of potentially improved quality under current scan conditions, and that they can yield from substantially reduced data images of quality comparable to that of current clinical CBCT images.
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