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

Synchrotron tomography is characterized by extremely brilliant X-rays, which enables almost artefact-free imaging. Furthermore, a very high resolution can be achieved by using specialized X-ray optics and the special design of synchrotron facilities also enables fast in-situ experiments, i.e. 4D tomography. The combination of these properties enables high-resolution computer tomography on objects where conventional laboratory CT fails. At the same time, this also generates enormous amounts of data that usually cannot be processed by the end user, pushing even the operators of the synchrotrons to their limits.

The overall goal of the KI4D4E project is to develop AI-based methods which can be used by end users to process the enormous amounts of data in such 4D CT measurements. This includes the improvement of image quality through artifact reduction, the reduction and accessibility of the data for end users to support them in interpreting the results.

The project focuses on the topics of artefact reduction, segmentation and visualization of large 4D datasets. The resulting methods should be applicable to data generated by both photon and neutron sources.

© Fraunhofer IIS
Schematic shape change/propagation of pores in a material, as they are often examined with in situ CT at the synchrotron.

  • Project title: An AI-based framework for the visualization and evaluation of massive amounts of 4D tomography data for end users of beamlines
  • Acronym: KI4D4E
  • Term: 36 months (start: 01.03.2023)
  • Funding body: Federal Ministry of Education and Research

  • Development of a framework for 4D tomography as a contribution to the digital infrastructure for
    • synchrotron radiation sources, and
    • neutron radiation sources.
  • Development of novel methods for handling terabyte-sized 4D computed tomography data on commercially available PCs
  • Reduction of 4D CT artifacts by the end user using AI-based correction methods
  • AI-based data reduction, e.g. via segmentation or derivation of secondary data

  • University of Stuttgart
  • University of Passau, Institute for Software Systems in Technical Applications of Computer Science (FORWISS)
  • Friedrich-Alexander-University Erlangen-Nuremberg
  • Karlsruhe Institute of Technology (KIT), Laboratory for Applications of Synchrotron Radiation
  • Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V., Development Center X-ray Technology of the Fraunhofer Institute for Integrated Circuits (IIS)
  • Helmholtz-Zentrum Berlin für Materialien und Energie GmbH
  • Helmholtz Center Hereon GmbH
  • Research Center Jülich

Associated partners: European Spallation Source (ESS), European Synchrotron Radiation Facility (ESRF), Helmholtz-Zentrum Hereon/German Engineering Materials Science Centre (GEMS), Institut Laue-Langevin (ILL), math2market GmbH, MITOS GmbH, Xploraytion GmbH