AI-enabled high-resolution scanning coherent diffraction imaging
Abstract
Ptychographic imaging is a powerful means of imaging beyond the resolution limits of typical x-ray optics. Capturing recovered images from raw ptychographic data, however, requires the solution of an inverse problem, namely, phase retrieval. Phase retrieval algorithms are computationally expensive, which precludes real-time imaging. In this work, we propose PtychoNN, an approach to solve the ptychography data inversion problem based on a deep convolutional neural network. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. Our results demonstrate the practical application of machine learning to recover high fidelity amplitude and phase contrast images of a real sample hundreds of times faster than current ptychography reconstruction packages. Furthermore, by overcoming the constraints of iterative model-based methods, we can significantly relax sampling constraints on data acquisition while still producing an excellent image of the sample. Besides drastically accelerating acquisition and analysis, this capability has profound implications for the imaging of dose sensitive, dynamic, and extremely voluminous samples.
- Authors:
-
[1];
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Stats Perform, Chicago, IL (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States). Advanced Photon Source (APS)
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
- OSTI Identifier:
- 1660559
- Alternate Identifier(s):
- OSTI ID: 1644153
- Grant/Contract Number:
- AC02-06CH11357; AC02- 06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Applied Physics Letters
- Additional Journal Information:
- Journal Volume: 117; Journal Issue: 4; Journal ID: ISSN 0003-6951
- Publisher:
- American Institute of Physics (AIP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 47 OTHER INSTRUMENTATION; Artificial Intelligence; X-ray microscopy; Artificial intelligence; Artificial neural networks; Nanomaterials; Machine learning; X-ray imaging; Ptychography; Synchrotron X-ray diffraction
Citation Formats
Cherukara, Mathew J., Zhou, Tao, Nashed, Youssef, Enfedaque, Pablo, Hexemer, Alex, Harder, Ross J., and Holt, Martin V. AI-enabled high-resolution scanning coherent diffraction imaging. United States: N. p., 2020.
Web. doi:10.1063/5.0013065.
Cherukara, Mathew J., Zhou, Tao, Nashed, Youssef, Enfedaque, Pablo, Hexemer, Alex, Harder, Ross J., & Holt, Martin V. AI-enabled high-resolution scanning coherent diffraction imaging. United States. https://doi.org/10.1063/5.0013065
Cherukara, Mathew J., Zhou, Tao, Nashed, Youssef, Enfedaque, Pablo, Hexemer, Alex, Harder, Ross J., and Holt, Martin V. Mon .
"AI-enabled high-resolution scanning coherent diffraction imaging". United States. https://doi.org/10.1063/5.0013065. https://www.osti.gov/servlets/purl/1660559.
@article{osti_1660559,
title = {AI-enabled high-resolution scanning coherent diffraction imaging},
author = {Cherukara, Mathew J. and Zhou, Tao and Nashed, Youssef and Enfedaque, Pablo and Hexemer, Alex and Harder, Ross J. and Holt, Martin V.},
abstractNote = {Ptychographic imaging is a powerful means of imaging beyond the resolution limits of typical x-ray optics. Capturing recovered images from raw ptychographic data, however, requires the solution of an inverse problem, namely, phase retrieval. Phase retrieval algorithms are computationally expensive, which precludes real-time imaging. In this work, we propose PtychoNN, an approach to solve the ptychography data inversion problem based on a deep convolutional neural network. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. Our results demonstrate the practical application of machine learning to recover high fidelity amplitude and phase contrast images of a real sample hundreds of times faster than current ptychography reconstruction packages. Furthermore, by overcoming the constraints of iterative model-based methods, we can significantly relax sampling constraints on data acquisition while still producing an excellent image of the sample. Besides drastically accelerating acquisition and analysis, this capability has profound implications for the imaging of dose sensitive, dynamic, and extremely voluminous samples.},
doi = {10.1063/5.0013065},
journal = {Applied Physics Letters},
number = 4,
volume = 117,
place = {United States},
year = {Mon Jul 27 00:00:00 EDT 2020},
month = {Mon Jul 27 00:00:00 EDT 2020}
}
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