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Please use this identifier to cite or link to this item: http://hdl.handle.net/1993/2818

Title: Local independence in computed tomography as a basis for parallel computing
Authors: Martin, Daniel Morris
Supervisor: Thulasiraman, Parimala (Computer Science) Gordon, Richard (Computer Science)
Examining Committee: Li, Ben (Computer Science) Morrison, Jason (Biosystems Engineering)
Graduation Date: October 2007
Keywords: parallel computing
computed tomography
algebraic reconstruction technique
data partitioning
Issue Date: 14-Sep-2007
Abstract: Iterative CT reconstruction algorithms are superior to the standard convolution backpropagation (CBP) methods when reconstructing from a small number of views (hence less radiation), but are computationally costly. To reduce the execution time, this work implements and tests a parallel approach to iterative algorithms using a cluster of workstations, which is a low cost system found in many offices and non-academic sites. A previous implementation showed little speedup because of the significant cost of inter-processor communication. In this thesis, several data partitioning methods are examined, including some image tiling methods that exploit the spatial locality demonstrated by local CT. Using these methods, computation can proceed locally, without the need for inter-processor communication during every iteration. A relative speedup of up to 17 times is obtained using 25 processors, demonstrating that good performance can be obtained running computationally intensive CT reconstruction algorithms on distributed memory hardware.
URI: http://hdl.handle.net/1993/2818
Appears in Collection(s):FGS - Electronic Theses & Dissertations (Public)

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