Deep learning for improving non-destructive grain mapping in 3D

H. Fang*, E. Hovad, Y. Zhang, L. K.H. Clemmensen, B. Kjaer Ersbøll, D. Juul Jensen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Laboratory X-ray diffraction contrast tomography (LabDCT) is a novel imaging technique for non-destructive 3D characterization of grain structures. An accurate grain reconstruction critically relies on precise segmentation of diffraction spots in the LabDCT images. The conventional method utilizing various filters generally satisfies segmentation of sharp spots in the images, thereby serving as a standard routine, but it also very often leads to over or under segmentation of spots, especially those with low signal-to-noise ratios and/or small sizes. The standard routine also requires a fine tuning of the filtering parameters. To overcome these challenges, a deep learning neural network is presented to efficiently and accurately clean the background noise, thereby easing the spot segmentation. The deep learning network is first trained with input images, synthesized using a forward simulation model for LabDCT in combination with a generic approach to extract features of experimental backgrounds. Then, the network is applied to remove the background noise from experimental images measured under different geometrical conditions for different samples. Comparisons of both processed images and grain reconstructions show that the deep learning method outperforms the standard routine, demonstrating significantly better grain mapping.

Original languageEnglish
Pages (from-to)719-731
Publication statusPublished - 2021


  • Background noise
  • Computer vision
  • Deep learning
  • Grain mapping
  • LabDCT
  • Spot segmentation
  • Tomography
  • X-ray diffraction


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