# Super Time‐Resolved Tomography

## TL;DR

A new X-ray imaging method called STRT improves time resolution by 10 times while keeping image quality, enabling better study of fast 3D processes.

## Contribution

STRT introduces a physics-informed deep learning algorithm for 4D X-ray imaging with enhanced temporal resolution.

## Key findings

- STRT achieves high-fidelity 3D reconstructions from limited angular data.
- STRT was validated using simulations and experiments on droplet collisions and additive manufacturing.
- STRT enables the study of rapid dynamic processes previously unattainable with conventional tomoscopy.

## 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) 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.

A super time‐resolved tomography (STRT) approach is presented to reconstruct 4D X‐ray movies with an order‐of‐magnitude improvement in temporal resolution without sacrificing spatial resolution. By leveraging a physics‐informed deep learning algorithm that shares spatiotemporal features, STRT achieves high‐fidelity 3D reconstructions from sparse‐view, limited‐angle data, enabling the investigation of rapid dynamic processes beyond the reach of conventional tomoscopy.