Evidence map›Paper›PMID 37024654›Full record

ArticleNature methods2023

Virtual-scanning light-field microscopy for robust snapshot high-resolution volumetric imaging.

Zhi Lu, Yu Liu, Manchang Jin, Xin Luo, Huanjing Yue, Zian Wang, Siqing Zuo, Yunmin Zeng, Jiaqi Fan, Yanwei Pang and 3 more

Abstract read
In one paragraph

Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Curvy Surface Reconstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  7. Article
  8. Article
  9. Light-field deep learning enables high-throughput, scattering-mitigated calcium imaging.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Zhi Lu *Department of Automation, Tsinghua University, Beijing, China.ORCID http://orcid.org/0000-0003-3739-8756
Yu Liu *School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Manchang JinSchool of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Xin LuoSchool of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Huanjing YueSchool of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Zian WangDepartment of Automation, Tsinghua University, Beijing, China.
Siqing ZuoDepartment of Automation, Tsinghua University, Beijing, China.
Yunmin ZengDepartment of Automation, Tsinghua University, Beijing, China.
Jiaqi FanInstitute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China.
Yanwei PangSchool of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Jiamin WuDepartment of Automation, Tsinghua University, Beijing, China. wujiamin@tsinghua.edu.cn.ORCID http://orcid.org/0000-0003-3479-1026
Jingyu YangSchool of Electrical and Information Engineering, Tianjin University, Tianjin, China. yjy@tju.edu.cn.ORCID http://orcid.org/0000-0002-7521-7920
Qionghai DaiDepartment of Automation, Tsinghua University, Beijing, China. qhdai@tsinghua.edu.cn.ORCID http://orcid.org/0000-0001-7043-3061

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-speed three-dimensional (3D) intravital imaging in animals is useful for studying transient subcellular interactions and functions in health and disease. Light-field microscopy (LFM) provides a computational solution for snapshot 3D imaging with low phototoxicity but is restricted by low resolution and reconstruction artifacts induced by optical aberrations, motion and noise. Here, we propose virtual-scanning LFM (VsLFM), a physics-based deep learning framework to increase the resolution of LFM up to the diffraction limit within a snapshot. By constructing a 40 GB high-resolution scanning LFM dataset across different species, we exploit physical priors between phase-correlated angular views to address the frequency aliasing problem. This enables us to bypass hardware scanning and associated motion artifacts. Here, we show that VsLFM achieves ultrafast 3D imaging of diverse processes such as the beating heart in embryonic zebrafish, voltage activity in Drosophila brains and neutrophil migration in the mouse liver at up to 500 volumes per second.

Indexed as

MicroscopyZebrafishAnimalsImaging, Three-DimensionalMice

Identifiers

PMID37024654
PMCPMC10172145

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.