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Publication Date

Summer 2012

Degree Type

Thesis - Campus Access Only

Degree Name

Master of Science (MS)

Department

Engineering

Advisor

Hua H. Li

Keywords

GPGPU, image processing, Micro-CT scanner, parallel computing

Subject Areas

Engineering; Biomedical engineering; Computer engineering

Abstract

As a result of recent technical advancements in Computed Tomography (CT), CT systems can achieve higher spatial resolution on a micrometer to a nanometer scale. This improvement allows us to observe the structure of an investigated object more precisely. The quest to increase spatial resolution has historically posed many challenges for CT developers. However, at this point, the performance of image processing in CT has been drastically improved as a result of the evolution of the Graphics Processing Unit (GPU), which utilizes massive numbers of processing cores operating in parallel. The GPU-computing methodology is called General Purpose GPU (GPGPU) computing. In general, deployment of a GPGPU-computing environment has become more affordable in recent years. Moreover, in the cloud-computing domain, a cluster technology that distributes computational tasks to multiple processing nodes has become available for GPGPU computing. The objectives of this research were 1) to conduct a literature survey focusing on both the historical development of CT technology and the present state-of-the-art; 2) to design, utilize, and test GPGPU computing in CT image-processing, the phase of the study that included mathematical formulation, verification, and improvement of the developed image-processing algorithm; and 3) to extend the GPGPU-computing system from a local standalone system environment to a cloud-computing environment.

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