Unsupervised Deep Learning for Laboratory-Based Diffraction Contrast Tomography

Abstract

An important leap forward for the 3D community is the possibility to perform non-destructive 3D microstructural imaging in the home laboratories. This possibility is profiled by a recently developed technique—laboratory X-ray diffraction contrast tomography (LabDCT). As diffraction spots in LabDCT images are the basis for 3D reconstruction of microstructures, it is critical to get their identification as precise as possible. In the present work we use a deep learning (DL) routine to optimize the identification of the spots. It is shown that by adding an artificial simple constant background noise to a series of forward simulated LabDCT diffraction images, DL can be trained and then learn to remove high frequency noise and low frequency radial gradients in brightness in the real experimental LabDCT images. The training of the DL routine is unsupervised in the sense that no human intervention is needed for labelling the data. The reduction in high frequency noise and low frequency radial gradients in brightness is demonstrated by comparing line profile scans through the experimental and the DL output images. Finally, the implications of this reduction procedure on the spot identification are analysed and possible improvements are discussed.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. 1.

    Poulsen H, Fæster S, Lauridsen E, Schmidt S, Suter R, Lienert U, Margulies L, Lorentzen T, Juul Jensen D (2001) Three-dimensional maps of grain boundaries and the stress state of individual grains in polycrystals and powders. J. Appl. Crystallogr. 34:751–756

    CAS  Article  Google Scholar 

  2. 2.

    Tischler J, Liu W, Xu R (2019) Micro-diffraction in 3D. In: Proceedings of 40th Risø international symposium on materials science: metal microstructures in 2D, 3D and 4D, pp 329–334

  3. 3.

    Zhang, Y (2019) Quantification of local boundary migration in 2D/3D. In: IOP conference series: materials science and engineering, vol 580, 012015

  4. 4.

    Bhattacharya A, Shen YF, Hefferan CM, Li SF, Lind J, Suter RM, Rohrer GS (2019) Three-dimensional observations of grain volume changes during annealing of polycrystalline Ni. Acta Mater 167:40–50

    CAS  Article  Google Scholar 

  5. 5.

    Hefferan CM, Lind J, Li SF, Lienert U, Rollett AD, Suter RM (2012) Observation of recovery and recrystallization in high-purity aluminum measured with forward modeling analysis of high-energy diffraction microscopy. Acta Mater 60(10):4311–4318

    CAS  Article  Google Scholar 

  6. 6.

    Bachmann F, Bale H, Gueninchault N, Holzner C, Lauridsen EM (2019) 3D grain reconstruction from laboratory diffraction contrast tomography. J Appl Crystallogr 52(3):643–651

    CAS  Article  Google Scholar 

  7. 7.

    Oddershede J, Sun J, Gueninchault N, Bachmann F, Bale H, Holzner C, Lauridsen E (2019) Non-destructive characterization of polycrystalline materials in 3d by laboratory diffraction contrast tomography. Integr Mater Manuf Innov 8(2):217–225

    Article  Google Scholar 

  8. 8.

    Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. https://arxiv.org/abs/1505.04597

  9. 9.

    Johnson J, Alahi A, Li FF (2016) Perceptual losses for real-time style transfer and super-resolution. https://arxiv.org/abs/1603.08155

  10. 10.

    Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. https://arxiv.org/abs/1409.1556

  11. 11.

    Fang H, Juul Jensen D, Zhang Y (2019) An efficient method to improve the spatial resolution of laboratory x-ray diffraction contrast tomography. In: IOP conference series: materials science and engineering, vol 580 (1) 012030

  12. 12.

    https://github.com/haixingfang/LabDCT-forward-simu-model

  13. 13.

    Boone JM, Seibert JA (1997) An accurate method for computer-generating tungsten anode x-ray spectra from 30 to 140 kv. Med Phys 24(11):1661–1670

    CAS  Article  Google Scholar 

  14. 14.

    Fang H, Juul Jensen D, Zhang Y (2020) A flexible and standalone forward simulation model for laboratory X-ray diffraction contrast tomography. Acta Crystallogr. https://doi.org/10.1107/S2053273320010852

    Article  Google Scholar 

  15. 15.

    https://github.com/fastai/course-v3/blob/master/nbs/dl1/lesson7-superres.ipynb

  16. 16.

    https://course.fast.ai/videos/?lesson=7

  17. 17.

    https://github.com/hiromis/notes/blob/master/Lesson7.md

Download references

Acknowledgements

This work is financially supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (M4D—Grant Agreement No. 788567).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Emil Hovad.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hovad, E., Fang, H., Zhang, Y. et al. Unsupervised Deep Learning for Laboratory-Based Diffraction Contrast Tomography. Integr Mater Manuf Innov (2020). https://doi.org/10.1007/s40192-020-00189-x

Download citation

Keywords

  • LabDCT
  • Deep learning
  • Noise reduction
  • Diffraction images
  • 3D microstructures