A Framework for the AI-based visualization and analysis of massive amounts of 4D tomography data for end users of beamlines

February 1, 2025·
S. Kieß
,
T. Lang
,
T. Sauer
,
A.M. Stock
,
A. Chernov
,
Y. Sun
,
A. Maier
,
T. Faragó
,
A. Ershov
,
G. Lefloch
,
G. Silva
,
T. Baumbach
,
S. Zabler
,
A. Hölzing
,
K. Dremel
,
A.R. Durmaz
,
A. Thomas
,
I. Manke
,
N. Kardjilov
,
T. Arlt
Tak Ming Wong
Tak Ming Wong
,
R. Willumeit-Römer
,
J. Moosmann
,
B. Zeller-Plumhoff
,
D. Froning
,
S. Simon
· 1 min read
Abstract
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.
Type
Publication
14th Conference on Industrial Computed Tomography (iCT) 2025
publications

How to cite

Kieß, S., Lang, T., Sauer, T., Stock, A., Chernov, A., Sun, Y., Maier, A., Faragó, T., Ershov, A., Lefloch, G., Silva, G., Baumbach, T., Zabler, S., Hölzing, A., Dremel, K., Durmaz, A., Thomas, A., Manke, I., Kardjilov, N., Arlt, T., Wong, T., Willumeit-römer, R., Moosmann, J., Zeller-Plumhoff, B., Froning, D., & Simon, S. (2025). A Framework for the AI-based visualization and analysis of massive amounts of 4D tomography data for end users of beamlines. 14th Conference on Industrial Computed Tomography (iCT), 4 - 7 February 2025, Antwerp, Belgium. e-Journal of Nondestructive Testing Vol. 30(2).

Bibtex

@article{kiess2025framework,
  title={A Framework for the AI-based Visualization and Analysis of Massive Amounts of 4D Tomography Data for End Users of Beamlines},
  author={Kie{\ss}, Steffen and Lang, Thomas and Sauer, Tomas and Stock, A Michael and Chernov, Andrei and Sun, Yipeng and Maier, Andreas and Farag{\'o}, Tom{\'a}{\v{s}} and Ershov, Alexey and Lefloch, Gabriel and others},
  year={2025}
}
CT Synchrotron Radiation CT (SRCT)
Tak Ming Wong
Authors
Tak Ming Wong (he/him)
Research Scientist
Tak Ming Wong is a research scientist at Helmholtz-Zentrum Hereon, Germany. His research interests are mainly in computational imaging, computer vision, scientific machine learning (SciML) for advanced imaging modalities.