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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1107/S1600577521011139
|2 doi
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|a pubmed24n1117.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Flenner, Silja
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Machine learning denoising of high-resolution X-ray nanotomography data
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Revised 05.11.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a open access.
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|a High-resolution X-ray nanotomography is a quantitative tool for investigating specimens from a wide range of research areas. However, the quality of the reconstructed tomogram is often obscured by noise and therefore not suitable for automatic segmentation. Filtering methods are often required for a detailed quantitative analysis. However, most filters induce blurring in the reconstructed tomograms. Here, machine learning (ML) techniques offer a powerful alternative to conventional filtering methods. In this article, we verify that a self-supervised denoising ML technique can be used in a very efficient way for eliminating noise from nanotomography data. The technique presented is applied to high-resolution nanotomography data and compared to conventional filters, such as a median filter and a nonlocal means filter, optimized for tomographic data sets. The ML approach proves to be a very powerful tool that outperforms conventional filters by eliminating noise without blurring relevant structural features, thus enabling efficient quantitative analysis in different scientific fields
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|a Journal Article
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4 |
|a Zernike phase contrast
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4 |
|a denoising
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4 |
|a full-field X-ray microscopy
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| 650 |
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|a machine learning
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| 650 |
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|a nanotomography
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| 700 |
1 |
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|a Bruns, Stefan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Longo, Elena
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Parnell, Andrew J
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Stockhausen, Kilian E
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Müller, Martin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Greving, Imke
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of synchrotron radiation
|d 1994
|g 29(2022), Pt 1 vom: 01. Jan., Seite 230-238
|w (DE-627)NLM09824129X
|x 1600-5775
|7 nnns
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| 773 |
1 |
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|g volume:29
|g year:2022
|g number:Pt 1
|g day:01
|g month:01
|g pages:230-238
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| 856 |
4 |
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|u http://dx.doi.org/10.1107/S1600577521011139
|3 Volltext
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|d 29
|j 2022
|e Pt 1
|b 01
|c 01
|h 230-238
|