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Super Time-Resolved Tomography

Hu, Zhe LU ; Yao, Zisheng LU orcid ; Josefsson, Kalle ; García-Moreno, Francisco ; Makowska, Malgorzata ; Zhang, Yuhe LU and Villanueva-Perez, Pablo LU orcid (2025) In Advanced Science
Abstract

Understanding three Dimensional (3D) fundamental processes is crucial for academic and industrial applications. Nowadays, X-ray time-resolved tomography, or tomoscopy, is a leading technique for in situ and operando 4D (3D+time) characterization. Despite its ability to achieve 1000 tomograms per second at large-scale X-ray facilities, its applicability is limited by the centrifugal forces exerted on samples and the challenges of developing suitable environments for such high-speed studies. Here, Super Time-Resolved Tomography (STRT) is introduced, an approach that has the potential to enhance the temporal resolution of tomoscopy by at least an order of magnitude while preserving spatial resolution. STRT exploits a 4D Deep Learning (DL)... (More)

Understanding three Dimensional (3D) fundamental processes is crucial for academic and industrial applications. Nowadays, X-ray time-resolved tomography, or tomoscopy, is a leading technique for in situ and operando 4D (3D+time) characterization. Despite its ability to achieve 1000 tomograms per second at large-scale X-ray facilities, its applicability is limited by the centrifugal forces exerted on samples and the challenges of developing suitable environments for such high-speed studies. Here, Super Time-Resolved Tomography (STRT) is introduced, an approach that has the potential to enhance the temporal resolution of tomoscopy by at least an order of magnitude while preserving spatial resolution. STRT exploits a 4D Deep Learning (DL) reconstruction algorithm to produce high-fidelity 3D reconstructions at each time point, retrieved from a significantly reduced angular range of a few degrees compared to the 0–180° of traditional tomoscopy. Thus, STRT enhances the temporal resolution compared to tomoscopy by a factor equal to the ratio between 180° and the angular ranges used by STRT. In this work, the 4D capabilities of STRT were validated through simulations and experiments on droplet collision simulations and additive manufacturing processes. It is anticipated that STRT will significantly expand the capabilities of 4D X-ray imaging, enabling previously unattainable studies in both academic and industrial contexts, such as materials formation and mechanical testing.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
additive manufacturing, machine learning, time-resolved tomography, X-ray imaging
in
Advanced Science
publisher
John Wiley & Sons Inc.
external identifiers
  • pmid:41164925
  • scopus:105020440262
ISSN
2198-3844
DOI
10.1002/advs.202511933
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2025 The Author(s). Advanced Science published by Wiley-VCH GmbH.
id
ffc7f8b4-435a-414d-9b99-4983390adc2a
date added to LUP
2026-01-14 15:30:49
date last changed
2026-01-15 03:41:24
@article{ffc7f8b4-435a-414d-9b99-4983390adc2a,
  abstract     = {{<p>Understanding three Dimensional (3D) fundamental processes is crucial for academic and industrial applications. Nowadays, X-ray time-resolved tomography, or tomoscopy, is a leading technique for in situ and operando 4D (3D+time) characterization. Despite its ability to achieve 1000 tomograms per second at large-scale X-ray facilities, its applicability is limited by the centrifugal forces exerted on samples and the challenges of developing suitable environments for such high-speed studies. Here, Super Time-Resolved Tomography (STRT) is introduced, an approach that has the potential to enhance the temporal resolution of tomoscopy by at least an order of magnitude while preserving spatial resolution. STRT exploits a 4D Deep Learning (DL) reconstruction algorithm to produce high-fidelity 3D reconstructions at each time point, retrieved from a significantly reduced angular range of a few degrees compared to the 0–180° of traditional tomoscopy. Thus, STRT enhances the temporal resolution compared to tomoscopy by a factor equal to the ratio between 180° and the angular ranges used by STRT. In this work, the 4D capabilities of STRT were validated through simulations and experiments on droplet collision simulations and additive manufacturing processes. It is anticipated that STRT will significantly expand the capabilities of 4D X-ray imaging, enabling previously unattainable studies in both academic and industrial contexts, such as materials formation and mechanical testing.</p>}},
  author       = {{Hu, Zhe and Yao, Zisheng and Josefsson, Kalle and García-Moreno, Francisco and Makowska, Malgorzata and Zhang, Yuhe and Villanueva-Perez, Pablo}},
  issn         = {{2198-3844}},
  keywords     = {{additive manufacturing; machine learning; time-resolved tomography; X-ray imaging}},
  language     = {{eng}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Advanced Science}},
  title        = {{Super Time-Resolved Tomography}},
  url          = {{http://dx.doi.org/10.1002/advs.202511933}},
  doi          = {{10.1002/advs.202511933}},
  year         = {{2025}},
}