Dynamic X-ray speckle-tracking imaging with high-accuracy phase retrieval based on deep learning Article Swipe
Fucheng Yu
,
Kang Du
,
Xiaolu Ju
,
Feixiang Wang
,
Ke Li
,
Can Chen
,
Guohao Du
,
Biao Deng
,
Honglan Xie
,
Tiqiao Xiao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1107/s2052252523010114
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1107/s2052252523010114
Speckle-tracking X-ray imaging is an attractive candidate for dynamic X-ray imaging owing to its flexible setup and simultaneous yields of phase, transmission and scattering images. However, traditional speckle-tracking imaging methods suffer from phase distortion at locations with abrupt changes in density, which is always the case for real samples, limiting the applications of the speckle-tracking X-ray imaging method. In this paper, we report a deep-learning based method which can achieve dynamic X-ray speckle-tracking imaging with high-accuracy phase retrieval. The calibration results of a phantom show that the profile of the retrieved phase is highly consistent with the theoretical one. Experiments of polyurethane foaming demonstrated that the proposed method revealed the evolution of the complicated microstructure of the bubbles accurately. The proposed method is a promising solution for dynamic X-ray imaging with high-accuracy phase retrieval, and has extensive applications in metrology and quantitative analysis of dynamics in material science, physics, chemistry and biomedicine.
Related Topics
Concepts
Speckle pattern
Phase retrieval
Tracking (education)
Imaging phantom
Computer science
Metrology
Phase (matter)
Optics
Artificial intelligence
Calibration
Distortion (music)
Speckle imaging
Computer vision
Physics
Pedagogy
Bandwidth (computing)
Amplifier
Computer network
Psychology
Quantum mechanics
Fourier transform
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1107/s2052252523010114
- https://journals.iucr.org/m/issues/2024/01/00/ti5027/ti5027.pdf
- OA Status
- gold
- Cited By
- 7
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389750004
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4389750004Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1107/s2052252523010114Digital Object Identifier
- Title
-
Dynamic X-ray speckle-tracking imaging with high-accuracy phase retrieval based on deep learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-14Full publication date if available
- Authors
-
Fucheng Yu, Kang Du, Xiaolu Ju, Feixiang Wang, Ke Li, Can Chen, Guohao Du, Biao Deng, Honglan Xie, Tiqiao XiaoList of authors in order
- Landing page
-
https://doi.org/10.1107/s2052252523010114Publisher landing page
- PDF URL
-
https://journals.iucr.org/m/issues/2024/01/00/ti5027/ti5027.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://journals.iucr.org/m/issues/2024/01/00/ti5027/ti5027.pdfDirect OA link when available
- Concepts
-
Speckle pattern, Phase retrieval, Tracking (education), Imaging phantom, Computer science, Metrology, Phase (matter), Optics, Artificial intelligence, Calibration, Distortion (music), Speckle imaging, Computer vision, Physics, Pedagogy, Bandwidth (computing), Amplifier, Computer network, Psychology, Quantum mechanics, Fourier transformTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2142618728, https://openalex.org/W2333204598, https://openalex.org/W1013390100, https://openalex.org/W3154482243, https://openalex.org/W2811391586, https://openalex.org/W1972147563, https://openalex.org/W2043287223, https://openalex.org/W2806757294, https://openalex.org/W2766421668, https://openalex.org/W3043221268, https://openalex.org/W3082260367, https://openalex.org/W2808192168, https://openalex.org/W2981186617, https://openalex.org/W2033986785, https://openalex.org/W2801396275, https://openalex.org/W1963483517, https://openalex.org/W2885673504, https://openalex.org/W3119647968, https://openalex.org/W4214730865, https://openalex.org/W3108073160, https://openalex.org/W2766857712, https://openalex.org/W3043800804, https://openalex.org/W2082637835, https://openalex.org/W2414814162, https://openalex.org/W2888054313, https://openalex.org/W2944779716, https://openalex.org/W2617767824, https://openalex.org/W2969468557, https://openalex.org/W3092551259, https://openalex.org/W2517108789, https://openalex.org/W2763453629, https://openalex.org/W3130166924, https://openalex.org/W4200550540, https://openalex.org/W2204897837, https://openalex.org/W2802367827, https://openalex.org/W3048003212, https://openalex.org/W3102127550, https://openalex.org/W3099018138, https://openalex.org/W4226201204 |
| referenced_works_count | 39 |
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