Super Time-Resolved Tomography

Zhe Hu

doi:10.1002/advs.202511933

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Super Time-Resolved Tomography

2025-10-30

Zhe Hu *

Zisheng Yao

Kalle Josefsson

Francisco García‐Moreno

Małgorzata G. Makowska

Yuhe Zhang

Pablo Villanueva‐Perez

DOI：10.1002/advs.202511933

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Abstract

En 中文

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.

Keywords:

additive manufacturing

machine learning

time-resolved tomography

X-ray imaging

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