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Local independence in computed tomography as a basis for parallel computing

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. / October 2007

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:MWU.anitoba.ca/dspace#1993/2818
Date14 September 2007
CreatorsMartin, Daniel Morris
ContributorsThulasiraman, Parimala (Computer Science) Gordon, Richard (Computer Science), Li, Ben (Computer Science) Morrison, Jason (Biosystems Engineering)
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
Languageen_US
Detected LanguageEnglish
Format5172649 bytes, application/pdf

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