A Framework for the AI-based visualization and analysis of massive amounts of 4D tomography data for end users of beamlines
The size of 4D tomography datasets acquired at synchrotron or neutron imaging facilities can reach several terabytes, which presents a significant challenge for their evaluation. This paper presents a framework that allows a compressed dataset to be kept in memory and makes it possible to evaluate...
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NDT.net
2025-02-01
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| Series: | e-Journal of Nondestructive Testing |
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| author | Steffen Kieß Thomas Lang Tomas Sauer Andreas Michael Stock Andrey Chernov Yipeng Sun Andreas K. Maier Tomáš Faragó Alexey Ershov Gabriel Lefloch Guilherme Silva Tilo Baumbach Simon Zabler Astrid Hölzing Kilian Dremel Ali Riza Durmaz Akhil Thomas Ingo Manke Nikolay Kardjilov Tobias Arlt Tak Ming Wong Regine Willumeit-römer Julian Moosmann Berit Zeller-Plumhoff Dieter Froning Sven Simon |
| author_facet | Steffen Kieß Thomas Lang Tomas Sauer Andreas Michael Stock Andrey Chernov Yipeng Sun Andreas K. Maier Tomáš Faragó Alexey Ershov Gabriel Lefloch Guilherme Silva Tilo Baumbach Simon Zabler Astrid Hölzing Kilian Dremel Ali Riza Durmaz Akhil Thomas Ingo Manke Nikolay Kardjilov Tobias Arlt Tak Ming Wong Regine Willumeit-römer Julian Moosmann Berit Zeller-Plumhoff Dieter Froning Sven Simon |
| author_sort | Steffen Kieß |
| collection | DOAJ |
| description |
The size of 4D tomography datasets acquired at synchrotron or neutron imaging facilities can reach several terabytes, which presents a significant challenge for their evaluation. This paper presents a framework that allows a compressed dataset to be kept in memory and makes it possible to evaluate and manipulate the dataset without requiring enough memory to decompress the entire dataset. The framework enables the compensation of imaging artifacts, including the compression artifacts of the 4D dataset, through the integration of neural networks. The reduction of imaging artifacts can be performed at the imaging facility or at the user's home institution. This framework reduces the computational burden on the computing infrastructure of large synchrotron and neutron facilities by allowing end users to process datasets on their institution's computers. This is made possible by compressing TBs of data to less than 128 GB, allowing powerful PCs to process TBs of 4D tomography data.
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| format | Article |
| id | doaj-art-dabb0bca98934265addf1ad266117dc5 |
| institution | Kabale University |
| issn | 1435-4934 |
| language | deu |
| publishDate | 2025-02-01 |
| publisher | NDT.net |
| record_format | Article |
| series | e-Journal of Nondestructive Testing |
| spelling | doaj-art-dabb0bca98934265addf1ad266117dc52025-02-06T10:48:18ZdeuNDT.nete-Journal of Nondestructive Testing1435-49342025-02-0130210.58286/30717A Framework for the AI-based visualization and analysis of massive amounts of 4D tomography data for end users of beamlinesSteffen Kießhttps://orcid.org/0009-0001-0899-0649Thomas Langhttps://orcid.org/0000-0001-5939-3919Tomas SauerAndreas Michael Stockhttps://orcid.org/0000-0003-2969-3990Andrey ChernovYipeng SunAndreas K. MaierTomáš FaragóAlexey ErshovGabriel LeflochGuilherme SilvaTilo BaumbachSimon ZablerAstrid HölzingKilian DremelAli Riza DurmazAkhil ThomasIngo MankeNikolay KardjilovTobias ArltTak Ming WongRegine Willumeit-römerJulian MoosmannBerit Zeller-PlumhoffDieter FroningSven Simon The size of 4D tomography datasets acquired at synchrotron or neutron imaging facilities can reach several terabytes, which presents a significant challenge for their evaluation. This paper presents a framework that allows a compressed dataset to be kept in memory and makes it possible to evaluate and manipulate the dataset without requiring enough memory to decompress the entire dataset. The framework enables the compensation of imaging artifacts, including the compression artifacts of the 4D dataset, through the integration of neural networks. The reduction of imaging artifacts can be performed at the imaging facility or at the user's home institution. This framework reduces the computational burden on the computing infrastructure of large synchrotron and neutron facilities by allowing end users to process datasets on their institution's computers. This is made possible by compressing TBs of data to less than 128 GB, allowing powerful PCs to process TBs of 4D tomography data. https://www.ndt.net/search/docs.php3?id=30717 |
| spellingShingle | Steffen Kieß Thomas Lang Tomas Sauer Andreas Michael Stock Andrey Chernov Yipeng Sun Andreas K. Maier Tomáš Faragó Alexey Ershov Gabriel Lefloch Guilherme Silva Tilo Baumbach Simon Zabler Astrid Hölzing Kilian Dremel Ali Riza Durmaz Akhil Thomas Ingo Manke Nikolay Kardjilov Tobias Arlt Tak Ming Wong Regine Willumeit-römer Julian Moosmann Berit Zeller-Plumhoff Dieter Froning Sven Simon A Framework for the AI-based visualization and analysis of massive amounts of 4D tomography data for end users of beamlines e-Journal of Nondestructive Testing |
| title | A Framework for the AI-based visualization and analysis of massive amounts of 4D tomography data for end users of beamlines |
| title_full | A Framework for the AI-based visualization and analysis of massive amounts of 4D tomography data for end users of beamlines |
| title_fullStr | A Framework for the AI-based visualization and analysis of massive amounts of 4D tomography data for end users of beamlines |
| title_full_unstemmed | A Framework for the AI-based visualization and analysis of massive amounts of 4D tomography data for end users of beamlines |
| title_short | A Framework for the AI-based visualization and analysis of massive amounts of 4D tomography data for end users of beamlines |
| title_sort | framework for the ai based visualization and analysis of massive amounts of 4d tomography data for end users of beamlines |
| url | https://www.ndt.net/search/docs.php3?id=30717 |
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