Evidence map›Paper›PMID 38882052›Full record

ArticleOptica2023

High-throughput deep tissue two-photon microscopy at kilohertz frame rates.

Sheng Xiao, John T Giblin, David A Boas, Jerome Mertz

Abstract read
In one paragraph

Article in Optica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Dual-channel event microscopy for ultrafast biological imaging.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  6. Article
  7. Dual-Channel Event Microscopy for Ultrafast Biological Imaging.bioRxiv : the preprint server for biology · 2025
    Article
  8. Article
  9. Non-inertial scan angle multiplier for expanded fields-of-view.bioRxiv : the preprint server for biology · 2025
    Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. 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

4 authors.

Sheng XiaoDepartment of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, USA.ORCID 0000-0001-8959-2388
John T GiblinDepartment of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, USA.
David A BoasDepartment of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, USA.
Jerome MertzDepartment of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, USA.ORCID 0000-0002-6343-9155

Funding

Multi-Layer Neuronal Imaging with Reverberation Multiphoton MicroscopyR01NS116139 · NINDS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI MERTZ, JEROME · 2020 to 2024
$2.1M
NINDS NIH HHS R01 NS116139
6 · The paper itself

Abstract

High-speed laser scanning microscopes are essential for monitoring fast biological phenomena. However, existing strategies that achieve millisecond time resolution with two-photon microscopes (2PMs) are generally technically challenging and suffer from compromises among imaging field of view, excitation efficiency, and depth penetration in thick tissue. Here, we present a versatile solution that enables a conventional video-rate 2PM to perform 2D scanning at kilohertz frame rates over large fields of view. Our system is based on implementation of a scan multiplier unit that provides inertia-free multiplication of the scanning speed while preserving all the benefits of standard 2PM. We demonstrate kilohertz subcellular-resolution 2PM imaging with an order of magnitude higher imaging throughput than previously achievable and penetration depths exceeding 500 μm, which we apply to the study of neurovascular coupling dynamics in the mouse brain.

Identifiers

PMID38882052
PMCPMC11178336

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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.