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4D-ONIX : A deep learning approach for reconstructing 3D movies from sparse X-ray projections

Zhang, Yuhe LU ; Yao, Zisheng LU ; Klöfkorn, Robert LU orcid ; Ritschel, Tobias and Villanueva Perez, Pablo LU orcid (2024) In arXiv.org
Abstract
The X-ray flux provided by X-ray free-electron lasers and storage rings offers new spatiotemporal possibilities to study in-situ and operando dynamics, even using single pulses of such facilities. X-ray Multi-Projection Imaging (XMPI) is a novel technique that enables volumetric information using single pulses of such facilities and avoids centrifugal forces induced by state-of-the-art time-resolved 3D methods such as time-resolved tomography. As a result, XMPI can acquire 3D movies (4D) at least three orders of magnitude faster than current methods. However, no algorithm can reconstruct 4D from highly sparse projections acquired by XMPI. Here, we present 4D-ONIX, a Deep Learning (DL)-based approach that learns to reconstruct 3D movies... (More)
The X-ray flux provided by X-ray free-electron lasers and storage rings offers new spatiotemporal possibilities to study in-situ and operando dynamics, even using single pulses of such facilities. X-ray Multi-Projection Imaging (XMPI) is a novel technique that enables volumetric information using single pulses of such facilities and avoids centrifugal forces induced by state-of-the-art time-resolved 3D methods such as time-resolved tomography. As a result, XMPI can acquire 3D movies (4D) at least three orders of magnitude faster than current methods. However, no algorithm can reconstruct 4D from highly sparse projections acquired by XMPI. Here, we present 4D-ONIX, a Deep Learning (DL)-based approach that learns to reconstruct 3D movies (4D) from an extremely limited number of projections. It combines the computational physical model of X-ray interaction with matter and state-of-the-art DL methods. We demonstrate the potential of 4D-ONIX to generate high-quality 4D by generalizing over multiple experiments with only two projections per timestamp for binary droplet collisions. We envision that 4D-ONIX will become an enabling tool for 4D analysis, offering new spatiotemporal resolutions to study processes not possible before. (Less)
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organization
publishing date
type
Working paper/Preprint
publication status
published
subject
in
arXiv.org
pages
23 pages
ISSN
2331-8422
DOI
10.48550/arXiv.2401.09508
language
English
LU publication?
yes
id
bbc0a1fa-825f-4de7-aefe-d458a480e64f
date added to LUP
2024-05-20 15:19:45
date last changed
2024-05-21 09:03:11
@misc{bbc0a1fa-825f-4de7-aefe-d458a480e64f,
  abstract     = {{The X-ray flux provided by X-ray free-electron lasers and storage rings offers new spatiotemporal possibilities to study in-situ and operando dynamics, even using single pulses of such facilities. X-ray Multi-Projection Imaging (XMPI) is a novel technique that enables volumetric information using single pulses of such facilities and avoids centrifugal forces induced by state-of-the-art time-resolved 3D methods such as time-resolved tomography. As a result, XMPI can acquire 3D movies (4D) at least three orders of magnitude faster than current methods. However, no algorithm can reconstruct 4D from highly sparse projections acquired by XMPI. Here, we present 4D-ONIX, a Deep Learning (DL)-based approach that learns to reconstruct 3D movies (4D) from an extremely limited number of projections. It combines the computational physical model of X-ray interaction with matter and state-of-the-art DL methods. We demonstrate the potential of 4D-ONIX to generate high-quality 4D by generalizing over multiple experiments with only two projections per timestamp for binary droplet collisions. We envision that 4D-ONIX will become an enabling tool for 4D analysis, offering new spatiotemporal resolutions to study processes not possible before.}},
  author       = {{Zhang, Yuhe and Yao, Zisheng and Klöfkorn, Robert and Ritschel, Tobias and Villanueva Perez, Pablo}},
  issn         = {{2331-8422}},
  language     = {{eng}},
  note         = {{Preprint}},
  series       = {{arXiv.org}},
  title        = {{4D-ONIX : A deep learning approach for reconstructing 3D movies from sparse X-ray projections}},
  url          = {{http://dx.doi.org/10.48550/arXiv.2401.09508}},
  doi          = {{10.48550/arXiv.2401.09508}},
  year         = {{2024}},
}