Evidence map›Paper›PMID 42380130›Full record

ArticleNature communications2026

Video-rate gigapixel ptychography via space-time neural field representations.

Ruihai Wang, Qianhao Zhao, Zhixuan Hong, Qiong Ma, Tianbo Wang, Lingzhi Jiang, Liming Yang, Shaowei Jiang, Feifei Huang, Thanh D Nguyen and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

17 authors.

Ruihai Wang *Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA.ORCID http://orcid.org/0009-0001-1780-4983
Qianhao Zhao *Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA. qianhao.zhao@uconn.edu.ORCID http://orcid.org/0009-0003-6899-5630
Zhixuan Hong *Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA.
Qiong MaDepartment of Biomedical Engineering, University of Connecticut, Storrs, CT, USA.
Tianbo WangDepartment of Biomedical Engineering, University of Connecticut, Storrs, CT, USA.
Lingzhi JiangDepartment of Biomedical Engineering, University of Connecticut, Storrs, CT, USA.
Liming YangDepartment of Biomedical Engineering, University of Connecticut, Storrs, CT, USA.
Shaowei JiangDepartment of Biomedical Engineering, University of Connecticut, Storrs, CT, USA.
Feifei HuangDepartment of Biomedical Engineering, University of Connecticut, Storrs, CT, USA.
Thanh D NguyenDepartment of Biomedical Engineering, University of Connecticut, Storrs, CT, USA.ORCID http://orcid.org/0000-0003-0813-4807
Leslie ShorDepartment of Chemical & Biomolecular Engineering, University of Connecticut, Storrs, CT, USA.
Daniel GageDepartment of Molecular and Cell Biology, University of Connecticut, Storrs, CT, USA.
Mary LiptonPacific Northwest National Laboratory, Department of Energy, Richland, WA, USA.
Christopher AndertonPacific Northwest National Laboratory, Department of Energy, Richland, WA, USA.ORCID http://orcid.org/0000-0002-6170-1033
Arunima BhattacharjeePacific Northwest National Laboratory, Department of Energy, Richland, WA, USA.
David BradyWyant College of Optical Sciences, University of Arizona, Tucson, AZ, USA.ORCID http://orcid.org/0000-0001-5655-2478
Guoan ZhengDepartment of Biomedical Engineering, University of Connecticut, Storrs, CT, USA. guoan.zheng@uconn.edu.ORCID http://orcid.org/0000-0002-4268-2690

Funding

Label-free digital cytopathology using deep-ultraviolet coded ptychography with intrinsic molecular contrastR01EB034744 · NIBIB · UNIVERSITY OF CONNECTICUT STORRS · PI Guoan Zheng · 2023 to 2026
$1.4M
NIBIB NIH HHS R01 EB034744U.S. Department of Energy (DOE) SC0025582U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01-EB034744
6 · The paper itself

Abstract

Achieving gigapixel space-bandwidth products (SBP) at video rates represents a fundamental challenge in imaging science. Here we demonstrate video-rate lensless ptychography that overcomes this barrier through the co-design of optics, sensing scheme, and computation: a coded surface encodes the dynamic object, time-sequential sensing spreads its information across frames, and a space-time neural-field framework recovers the dynamics by exploiting spatiotemporal correlations. Our approach transforms SBP scaling from sequential measurements to efficient correlation extraction. We demonstrate video-rate gigapixel imaging with centimeter-scale coverage while resolving 308-nm linewidths, achieving an SBP throughput of 20.8 gigapixels per second. Experimental validations span from monitoring mesoscale dynamics of snowflakes, bacteria, stem cells, microneedles, to characterizing time-varying dynamics in extreme-ultraviolet (EUV) experiments, demonstrating versatility across wavelengths. By transforming temporal variations from a constraint into exploitable correlations, we enable single-sensor gigapixel video that extends naturally to short-wavelength and electron regimes where radiation sensitivity has traditionally precluded high-resolution dynamic imaging.

Identifiers

PMID42380130
PMCPMC13458346

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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.