Effective Parallelization Strategies for Scalable, High-Performance Iterative Reconstruction

dc.contributor.authorGribble, Christiaan Paulen_US
dc.contributor.editorFrey, Steffen and Huang, Jian and Sadlo, Filipen_US
dc.date.accessioned2020-05-24T13:24:38Z
dc.date.available2020-05-24T13:24:38Z
dc.date.issued2020
dc.description.abstractIterative reconstruction techniques in X-ray computed tomography converge to a result by successively refining increasingly accurate estimates. Compared to alternative approaches, iterative reconstruction imposes significant computational demand but generally leads to higher reconstruction quality and is more robust to inherently imperfect scan data. We explore several strategies for exploiting parallelism in iterative reconstruction and evaluate their scalability and performance on modern workstation-class systems. Results show that scalable, high performance iterative reconstruction is possible with careful attention to the expression of parallelism in both the projection and backprojection phases of computation.en_US
dc.description.sectionheadersVisualization
dc.description.seriesinformationEurographics Symposium on Parallel Graphics and Visualization
dc.identifier.doi10.2312/pgv.20201074
dc.identifier.isbn978-3-03868-107-6
dc.identifier.issn1727-348X
dc.identifier.pages47-56
dc.identifier.urihttps://doi.org/10.2312/pgv.20201074
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pgv20201074
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.subjectComputing methodologies
dc.subjectParallel algorithms
dc.subjectRay tracing
dc.titleEffective Parallelization Strategies for Scalable, High-Performance Iterative Reconstructionen_US
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