Evidence map›Paper›PMID 41417604›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

High-speed X-ray tomography for 4D imaging.

Ivan Grega, William Whitney, Vikram Sudhir Deshpande

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. High-speed in situ tomographic imaging at lab scale.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Ivan GregaDepartment of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom.ORCID 0000-0002-0163-7009
William WhitneyGoogle DeepMind, San Francisco, CA 94105.
Vikram Sudhir DeshpandeDepartment of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom.ORCID 0000-0003-3899-3573

Funding

DOD | USN | Office of Naval Research (ONR) N00014-23-1-2797UK Research and Innovation (UKRI) EP/X02394X/1
6 · The paper itself

Abstract

Capturing high-rate spatiotemporal deformation of materials in three dimensions (3D) remains a significant challenge with current X-ray imaging techniques. We present a methodology that combines advances in neural rendering techniques with volume correlation methods to accurately reconstruct complex, high-rate 3D spatiotemporal structural evolutions. The fidelity and versatility of the method, which requires no pretraining, are demonstrated for a diverse set of intricate 3D-printed microarchitected solids. Using laboratory-based X-ray tomography, we capture the 3D growth of a high-rate crush band on a timescale of less than 100 ms. By broadening this idea to a stereo X-ray concept, we eliminate the need to rotate the image object, thereby extending the technique to significantly faster timescales. Our neural rendering framework opens possibilities for 3D observations of viscoelastic responses of biological materials to 3D investigations of numerous poorly understood dynamic processes, such as the runaway failure of batteries, all using laboratory X-ray systems.

Indexed as

dynamicneural renderingtomography

Identifiers

PMID41417604
PMCPMC12745763

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.