Effective Super-Resolution X-ray Tomography using MSDnet for Nondestructive Testing of Metallic Lattices: Analysis of Training Dynamics and Strategies

1. Withers, P.J., Bouman, C., Carmignato, S., Cnudde, V., Grimaldi, D., Hagen, C.K., Maire, E., Manley, M., Plessis, A., Stock, S.R.: X-ray computed tomography. Nat. Rev. Methods Prim. 1(1), 18 (2021). https://doi.org/10.1038/s43586-021-00015-410.1038/s43586-021-00015-4 ↗

2. Plessis, A., Yadroitsava, I., Yadroitsev, I.: Effects of defects on mechanical properties in metal additive manufacturing: A review focusing on X-ray tomography insights. Mater. Des. 187, 108385 (2020). https://doi.org/10.1016/j.matdes.2019.10838510.1016/j.matdes.2019.108385 ↗

3. Echeta, I., Feng, X., Dutton, B., Leach, R., Piano, S.: Review of defects in lattice structures manufactured by powder bed fusion. Int. J. Adv. Manuf. Technol. 106(5–6), 2649–2668 (2020). https://doi.org/10.1007/s00170-019-04753-410.1007/s00170-019-04753-4 ↗

4. Maconachie, T., Leary, M., Lozanovski, B., Zhang, X., Qian, M., Faruque, O., Brandt, M.: SLM lattice structures: Properties, performance, applications and challenges. Mater. Des. 183, 108137 (2019). https://doi.org/10.1016/j.matdes.2019.10813710.1016/j.matdes.2019.108137 ↗

5. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016). https://doi.org/10.1109/TPAMI.2015.243928110.1109/TPAMI.2015.2439281 ↗

6. Wang, G., Ye, J.C., Man, B.: Deep learning for tomographic image reconstruction. Nat. Mach. Intell. 2(12), 737–748 (2020). https://doi.org/10.1038/s42256-020-00273-z10.1038/s42256-020-00273-z ↗

7. Li, Y., Sixou, B., Peyrin, F.: A review of the deep learning methods for medical images super resolution problems. IRBM 42(2), 120–133 (2021). https://doi.org/10.1016/j.irbm.2020.08.00410.1016/j.irbm.2020.08.004 ↗

8. Hendriksen, A.A., Pelt, D.M., Palenstijn, W.J., Coban, S.B., Batenburg, K.J.: On-the-fly machine learning for improving image resolution in tomography. Appl. Sci. 9(12), 2445 (2019). https://doi.org/10.3390/app912244510.3390/app9122445 ↗

9. Frazer, L.L., Louis, N., Zbijewski, W., Vaishnav, J., Clark, K., Nicolella, D.P.: Super-resolution of clinical CT: Revealing microarchitecture in whole bone clinical CT image data. Bone 185, 117115 (2024). https://doi.org/10.1016/j.bone.2024.11711510.1016/j.bone.2024.117115 ↗

10. Bukka, V.V.R., Xu, M., Andrew, M., Andreyev, A.: Assessment of deep-learning-based resolution recovery algorithm relative to imaging system resolution and feature size. Methods Microsc. (2025). https://doi.org/10.1515/mim-2024-001710.1515/mim-2024-0017 ↗

11. Senck, S., Weinberger, P., Haghofer, A., Wögerer, B., Glinz, J., Yosifov, M., Behammer, L., Nepelius, L., Kastner, J., Trieb, K., Kranioti, E., Winkler, S.: Improving image resolution in microcomputed tomography data of Tarsal bones using super-resolution (2024). https://doi.org/10.2139/ssrn.482076110.2139/ssrn.4820761 ↗

12. Zhou, Y., Klintström, E., Klintström, B., Ferguson, S.J., Helgason, B., Persson, C.: A convolutional neural network-based method for the generation of super-resolution 3D models from clinical CT images. Comput. Methods Programs Biomed. 245, 108009 (2024). https://doi.org/10.1016/j.cmpb.2024.10800910.1016/j.cmpb.2024.108009 ↗

13. Furat, O., Finegan, D.P., Yang, Z., Kirstein, T., Smith, K., Schmidt, V.: Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks. NPJ Computat. Mater. 8(1), 68 (2022). https://doi.org/10.1038/s41524-022-00749-z10.1038/s41524-022-00749-z ↗

14. Kodama, M., Takeuchi, A., Uesugi, M., Hirai, S.: Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data. Energy and AI 14, 100305 (2023). https://doi.org/10.1016/j.egyai.2023.10030510.1016/j.egyai.2023.100305 ↗

15. Villarraga-Gómez, H., Crosby, K., Terada, M., Rad, M.N.: Assessing electronics with advanced 3D X-ray imaging techniques, nanoscale tomography, and deep learning. J. Fail. Anal. Prev. 24(5), 2113–2128 (2024). https://doi.org/10.1007/s11668-024-01989-510.1007/s11668-024-01989-5 ↗

16. Villarraga-Gómez, H., Frederick, C., Brackman, P., Andreyev, A., Trenikhina, Y., Johnson, N., Bale, H.: Non-destructive characterization of additive manufacturing components with computed tomography and 3D X-ray microscopy. e-J. Nondestruct. Test. 29(3) (2024). https://doi.org/10.58286/2925910.58286/29259 ↗

17. Yang, H., Wang, Z., Liu, X., Li, C., Xin, J., Wang, Z.: Deep learning in medical image super resolution: A review. Appl. Intell. 53(18), 20891–20916 (2023). https://doi.org/10.1007/s10489-023-04566-910.1007/s10489-023-04566-9 ↗

18. Fok, W.Y.R., Fieselmann, A., Herbst, M., Ritschl, L., Kappler, S., Saalfeld, S.: Deep learning in computed tomography super resolution using multi-modality data training. Med. Phys. 51(4), 2846–2860 (2024). https://doi.org/10.1002/mp.1682510.1002/mp.16825 ↗

19. Liu, X., Rüttgers, M., Quercia, A., Egele, R., Pfaehler, E., Shende, R., Aach, M., Schröder, W., Balaprakash, P., Lintermann, A.: Refining computer tomography data with super-resolution networks to increase the accuracy of respiratory flow simulations. Futur. Gener. Comput. Syst. 159, 474–488 (2024). https://doi.org/10.1016/j.future.2024.05.02010.1016/j.future.2024.05.020 ↗

20. Hou, Y., Canul-Ku, M., Cui, X., Zhu, M.: Super-resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning. X-Ray Spectrom. 53(5), 405–414 (2024). https://doi.org/10.1002/xrs.338910.1002/xrs.3389 ↗

21. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/TIP.2003.81986110.1109/TIP.2003.819861 ↗

22. Pelt, D.M., Sethian, J.A.: A mixed-scale dense convolutional neural network for image analysis. Proc. Natl. Acad. Sci. 115(2), 254–259 (2018). https://doi.org/10.1073/pnas.171583211410.1073/pnas.1715832114 ↗

23. Klos, A., Salvo, L., Lhuissier, P.: Multi-resolution X-ray tomography of a lattice structure for super-resolution using deep learning. Recherche Data Gouv (2025). https://doi.org/10.57745/VDKKAH

24. Stamati, O., Andò, E., Roubin, E., Cailletaud, R., Wiebicke, M., Pinzon, G., Couture, C., Hurley, R., Caulk, R., Caillerie, D., Matsushima, T., Bésuelle, P., Bertoni, F., Arnaud, T., Laborin, A., Rorato, R., Sun, Y., Tengattini, A., Okubadejo, O., Colliat, J.-B., Saadatfar, M., Garcia, F., Papazoglou, C., Vego, I., Brisard, S., Dijkstra, J., Birmpilis, G.: Spam: Software for practical analysis of materials. J. Open Source Softw. 5(51), 2286 (2020). https://doi.org/10.21105/joss.0228610.21105/joss.02286 ↗

25. Pelt, D., Batenburg, K., Sethian, J.: Improving tomographic reconstruction from limited data using mixed-scale dense convolutional neural networks. J. Imaging. 4(11), 128 (2018). https://doi.org/10.3390/jimaging411012810.3390/jimaging4110128 ↗

26. Walt, S., Schönberger, J.L., Nunez-Iglesias, J., Boulogne, F., Warner, J.D., Yager, N., Gouillart, E., Yu, T.: Scikit-image: Image processing in Python. PeerJ 2, 453 (2014). https://doi.org/10.7717/peerj.45310.7717/peerj.453 ↗

27. Klos, A., Salvo, L., Lhuissier, P.: Super-resolution X-ray tomography using deep learning applied to the 3D quantification of defects in lattice structures. Sci. Rep. (2025). https://doi.org/10.1038/s41598-025-20372-410.1038/s41598-025-20372-4 ↗

28. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979). https://doi.org/10.1109/TSMC.1979.431007610.1109/TSMC.1979.4310076 ↗

29. Steinhilber, F., Lachambre, J., Coeurjolly, D., Buffiere, J.-Y., Martin, G., Dendievel, R.: A methodology for the 3D characterization of surfaces using X-ray computed tomography: Application to additively manufactured parts. Addit. Manuf. 84, 104144 (2024). https://doi.org/10.1016/j.addma.2024.10414410.1016/j.addma.2024.104144 ↗