Real-time tilt undersampling optimization during electron tomography of beam sensitive samples using golden ratio scanning and RECAST3D
Source abstract
Abstract
Electron tomography is a widely used technique for 3D structural analysis of nanomaterials, but it can cause damage to samples due to high electron doses and long exposure times. To minimize such damage, researchers often reduce beam exposure by acquiring fewer projections through tilt undersampling. However, this approach can also introduce reconstruction artifacts due to insufficient sampling. Therefore, it is important to determine the optimal number of projections that minimizes both beam exposure and undersampling artifacts for accurate reconstructions of beam-sensitive samples. Current methods for determining this optimal number of projections involve acquiring and post-processing multiple reconstructions with different numbers of projections, which can be time-consuming and requires multiple samples due to sample damage. To improve this process, we propose a protocol that combines golden ratio scanning and quasi-3D reconstruction to estimate the optimal number of projections in real-time during a single acquisition. This protocol was validated using simulated and realistic nanoparticles, and was successfully applied to reconstruct two beam-sensitive metal-organic framework complexes.
Source: openalex_inverted_index · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Electron Microscopy Techniques and Applications · Electron and X-Ray Spectroscopy Techniques · Machine Learning in Materials Science
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Electron Microscopy for Materials Science and NANOlab Center of Excellence, University of Antwerp, Groenenborgerlaan 171, Antwerp 2020, Belgium
Centrum Wiskunde & Informatica, Science Park 123, Amsterdam 1098 XG, The Netherlands
Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333CA Leiden, The Netherlands
Funding
H2020 European Research Council · H2020 Marie Skłodowska-Curie Actions
References (48)
Show source-provided references
1. Roduner · Chem. Soc. Rev. · 2006 · 35 · 58310.1039/b502142c ↗
2. Laux · Arch. Toxicol. · 2018 · 92 · 12110.1007/s00204-017-2144-1 ↗
3. Barreto · Adv. Mater. · 2011 · 23 · H1810.1002/adma.201100140 ↗
4. Calvaresi · Nat. Nanotechnol. · 2020 · 15 · 51210.1038/s41565-020-0689-2 ↗
5. Choo · J. Am. Chem. Soc. · 2021 · 143 · 455010.1021/jacs.1c00850 ↗
6. Urban · Science · 2008 · 321 · 50610.1126/science.1152800 ↗
7. Crewe · J. Appl. Phys. · 1968 · 39 · 586110.1063/1.1656079 ↗
8. Shin · Appl. Phys. Lett. · 1989 · 55 · 245610.1063/1.102297 ↗
9. Kübel · Microsc. Microanal. · 2005 · 11 · 37810.1017/S1431927605050361 ↗
10. Midgley · Nat. Mater. · 2009 · 8 · 27110.1038/nmat2406 ↗
11. Scott · Nature · 2012 · 483 · 44410.1038/nature10934 ↗
12. Bartesaghi · J. Struct. Biol. · 2008 · 162 · 43610.1016/j.jsb.2008.02.008 ↗
13. Goris · Ultramicroscopy · 2012 · 113 · 12010.1016/j.ultramic.2011.11.004 ↗
14. Pryor · Sci. Rep. · 2017 · 7 · 110.1038/s41598-017-09847-1 ↗
15. D. E.Dudgeon and R. M.Mersereau , Multidimensional digital signal processing , Prentice-Hall , 1984
16. Gordon · J. Theor. Biol. · 1970 · 29 · 47110.1016/0022-5193(70)90109-8 ↗
17. Lange · J. Comput. Assist. Tomogr. · 1984 · 8 · 306
18. Ugurlu · Phys. Rev. B: Condens. Matter Mater. Phys. · 2011 · 83 · 11340810.1103/PhysRevB.83.113408 ↗
19. Crespi · Phys. Rev. B: Condens. Matter Mater. Phys. · 1996 · 54 · 592710.1103/PhysRevB.54.5927 ↗
20. Russo · Ultramicroscopy · 2018 · 187 · 4310.1016/j.ultramic.2018.01.009 ↗
21. Banhart · J. Mater. Sci. · 2006 · 41 · 450510.1007/s10853-006-0081-0 ↗
22. Jiang · Rep. Prog. Phys. · 2015 · 79 · 01650110.1088/0034-4885/79/1/016501 ↗
23. Liu · Commun. Chem. · 2020 · 3 · 110.1038/s42004-019-0249-6 ↗
24. Żak · Micron · 2021 · 145 · 10305810.1016/j.micron.2021.103058 ↗
25. Turner · Chem. Mater. · 2008 · 20 · 562210.1021/cm801165s ↗
26. Treacy · Ultramicroscopy · 1987 · 23 · 41110.1016/0304-3991(87)90252-X ↗
27. Egerton · Microsc. Res. Tech. · 2012 · 75 · 155010.1002/jemt.22099 ↗
28. McMullan · Ultramicroscopy · 2014 · 147 · 15610.1016/j.ultramic.2014.08.002 ↗
29. Chen · J. Am. Chem. Soc. · 2018 · 140 · 991210.1021/jacs.8b04457 ↗
30. Vanrompay · Ultramicroscopy · 2021 · 221 · 11319110.1016/j.ultramic.2020.113191 ↗
31. Vanrompay · Nanoscale · 2018 · 10 · 2279210.1039/C8NR08376B ↗
32. Cho · Opt. Eng. · 2012 · 51 · 08050110.1117/1.OE.51.8.080501 ↗
33. Davison · SIAM J. Appl. Math. · 1983 · 43 · 42810.1137/0143028 ↗
34. Vanrompay · Part. Part. Syst. Charact. · 2019 · 36 · 190009610.1002/ppsc.201900096 ↗
35. Frikel · Inverse Probl. · 2013 · 29 · 12500710.1088/0266-5611/29/12/125007 ↗
36. Batenburg · IEEE Trans. Image Process. · 2011 · 20 · 254210.1109/TIP.2011.2131661 ↗
37. Vanrompay · Part. Part. Syst. Charact. · 2020 · 37 · 200007310.1002/ppsc.202000073 ↗
38. Kaestner · Opt. Eng. · 2011 · 50 · 12320110.1117/1.3660298 ↗
39. Buurlage · Meas. Sci. Technol. · 2018 · 29 · 06400510.1088/1361-6501/aab754 ↗
40. Otsu · IEEE Trans. Syst. Man Cybern. · 1979 · 9 · 6210.1109/TSMC.1979.4310076 ↗
41. Heymann · Comput. Methods Programs Biomed. · 2022 · 220 · 10679910.1016/j.cmpb.2022.106799 ↗
42. Van Aarle · Opt. Express · 2016 · 24 · 2512910.1364/OE.24.025129 ↗
43. Van Aarle · Ultramicroscopy · 2015 · 157 · 3510.1016/j.ultramic.2015.05.002 ↗
44. Palenstijn · J. Struct. Biol. · 2011 · 176 · 25010.1016/j.jsb.2011.07.017 ↗
45. Goris · Nano Lett. · 2014 · 14 · 322010.1021/nl500593j ↗
46. Aulakh · J. Am. Chem. Soc. · 2019 · 141 · 299710.1021/jacs.8b11374 ↗
47. Rösler · Eur. J. Inorg. Chem. · 2014 · 2014 · 551410.1002/ejic.201402409 ↗
48. Buurlage · Sci. Rep. · 2019 · 9 · 110.1038/s41598-019-54647-4 ↗