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A high-throughput system for high-quality tomographic reconstruction of large datasets at Diamond Light Source
Tomographic datasets collected at synchrotrons are becoming very large and complex, and, therefore, need to be managed efficiently. Raw images may have high pixel counts, and each pixel can be multidimensional and associated with additional data such as those derived from spectroscopy. In time-resol...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4424489/ https://www.ncbi.nlm.nih.gov/pubmed/25939626 http://dx.doi.org/10.1098/rsta.2014.0398 |
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author | Atwood, Robert C. Bodey, Andrew J. Price, Stephen W. T. Basham, Mark Drakopoulos, Michael |
author_facet | Atwood, Robert C. Bodey, Andrew J. Price, Stephen W. T. Basham, Mark Drakopoulos, Michael |
author_sort | Atwood, Robert C. |
collection | PubMed |
description | Tomographic datasets collected at synchrotrons are becoming very large and complex, and, therefore, need to be managed efficiently. Raw images may have high pixel counts, and each pixel can be multidimensional and associated with additional data such as those derived from spectroscopy. In time-resolved studies, hundreds of tomographic datasets can be collected in sequence, yielding terabytes of data. Users of tomographic beamlines are drawn from various scientific disciplines, and many are keen to use tomographic reconstruction software that does not require a deep understanding of reconstruction principles. We have developed Savu, a reconstruction pipeline that enables users to rapidly reconstruct data to consistently create high-quality results. Savu is designed to work in an ‘orthogonal’ fashion, meaning that data can be converted between projection and sinogram space throughout the processing workflow as required. The Savu pipeline is modular and allows processing strategies to be optimized for users' purposes. In addition to the reconstruction algorithms themselves, it can include modules for identification of experimental problems, artefact correction, general image processing and data quality assessment. Savu is open source, open licensed and ‘facility-independent’: it can run on standard cluster infrastructure at any institution. |
format | Online Article Text |
id | pubmed-4424489 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | The Royal Society Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-44244892015-06-13 A high-throughput system for high-quality tomographic reconstruction of large datasets at Diamond Light Source Atwood, Robert C. Bodey, Andrew J. Price, Stephen W. T. Basham, Mark Drakopoulos, Michael Philos Trans A Math Phys Eng Sci Articles Tomographic datasets collected at synchrotrons are becoming very large and complex, and, therefore, need to be managed efficiently. Raw images may have high pixel counts, and each pixel can be multidimensional and associated with additional data such as those derived from spectroscopy. In time-resolved studies, hundreds of tomographic datasets can be collected in sequence, yielding terabytes of data. Users of tomographic beamlines are drawn from various scientific disciplines, and many are keen to use tomographic reconstruction software that does not require a deep understanding of reconstruction principles. We have developed Savu, a reconstruction pipeline that enables users to rapidly reconstruct data to consistently create high-quality results. Savu is designed to work in an ‘orthogonal’ fashion, meaning that data can be converted between projection and sinogram space throughout the processing workflow as required. The Savu pipeline is modular and allows processing strategies to be optimized for users' purposes. In addition to the reconstruction algorithms themselves, it can include modules for identification of experimental problems, artefact correction, general image processing and data quality assessment. Savu is open source, open licensed and ‘facility-independent’: it can run on standard cluster infrastructure at any institution. The Royal Society Publishing 2015-06-13 /pmc/articles/PMC4424489/ /pubmed/25939626 http://dx.doi.org/10.1098/rsta.2014.0398 Text en http://creativecommons.org/licenses/by/4.0/ © 2015 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Articles Atwood, Robert C. Bodey, Andrew J. Price, Stephen W. T. Basham, Mark Drakopoulos, Michael A high-throughput system for high-quality tomographic reconstruction of large datasets at Diamond Light Source |
title | A high-throughput system for high-quality tomographic reconstruction of large datasets at Diamond Light Source |
title_full | A high-throughput system for high-quality tomographic reconstruction of large datasets at Diamond Light Source |
title_fullStr | A high-throughput system for high-quality tomographic reconstruction of large datasets at Diamond Light Source |
title_full_unstemmed | A high-throughput system for high-quality tomographic reconstruction of large datasets at Diamond Light Source |
title_short | A high-throughput system for high-quality tomographic reconstruction of large datasets at Diamond Light Source |
title_sort | high-throughput system for high-quality tomographic reconstruction of large datasets at diamond light source |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4424489/ https://www.ncbi.nlm.nih.gov/pubmed/25939626 http://dx.doi.org/10.1098/rsta.2014.0398 |
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