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Accelerated Tomographic Image Recons...

Pan, Hui.

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Accelerated Tomographic Image Reconstruction of SPECT- CT Using GPU Parallelization.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Accelerated Tomographic Image Reconstruction of SPECT- CT Using GPU Parallelization./
Author:	Pan, Hui.
Description:	135 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.
Contained By:	Dissertation Abstracts International76-12B(E).
Subject:	Molecular biology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3664017
ISBN:	9781339099361

Accelerated Tomographic Image Reconstruction of SPECT- CT Using GPU Parallelization.
Pan, Hui.

Accelerated Tomographic Image Reconstruction of SPECT- CT Using GPU Parallelization. - 135 p.

Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.

Thesis (Ph.D.)--Florida Institute of Technology, 2015.

A graphics processing unit (GPU), also occasionally called a visual processing unit (VPU), is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. With the increasing needs of the very active computer graphics development community, the GPU has become an integral part of today's mainstream computing systems. Especially over the past six years, GPUs have been evolving at a rapid rate. Due to the massively parallel architecture and relatively low cost, GPUs have become powerful platforms for scientific computation. For tomography, iterative reconstruction algorithms pose tremendous computational challenges due to the massive computation requirements. GPUs provide an affordable platform to these requirements. In this work, we developed some GPU enabled algorithms to make use of acceleration techniques to speed up the reconstruction processing. Single Photon Emission Computed Tomography (SPECT) can require two types of images: static and dynamic. In the static case, we parallelized the Maximum likelihood Expectation Maximization Algorithm (MLEM), Ordered-Subsets Expectation Maximization (OSEM), Computed Tomography (CT), and the Point Spread Function algorithm (PSF). In the dynamic case, we parallelized the dynamic MLEM. All the algorithms performances are validated by the same algorithms but in the CPU version. For each algorithm, as the precondition for the same reconstructed results, we compared the experiment evaluation of scalability between the CPU and GPU versions. Moreover, we reorganized the GPU thread balancing to improve the GPU algorithm performance. In addition, we developed a data organization system, which is called ReMI, to prevent data loss or corruption. Our all experiments dataset were downloaded from this system.

ISBN: 9781339099361Subjects--Topical Terms:

517296
Molecular biology.

Accelerated Tomographic Image Reconstruction of SPECT- CT Using GPU Parallelization.
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