Evidence map›Paper›PMID 31465349›Full record

ArticleOptics letters2019

Noninvasive, in vivo rodent brain optical coherence tomography at 2.1  microns.

Jun Zhu, Shau Poh Chong, Wenjun Zhou, Vivek J Srinivasan

Abstract read
In one paragraph

Article in Optics letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. ImprovedJournal of biomedical optics · 2023
    Article
  6. Review
  7. Article
  8. Article
  9. 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.

Jun Zhu
Shau Poh Chong
Wenjun Zhou
Vivek J Srinivasan

Funding

Imaging Neuronal and Capillary Dysfunction Deep in the Rodent Brain in vivo Using 1700 NM Optical Coherence Microscopy and Tracer-Based KineticsR01NS094681 · NINDS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI SRINIVASAN, VIVEK JAY · 2015 to 2019
$1.6M
Development, Validation and Application of Metabolic Imaging in GlaucomaR01EY028287 · NEI · STANFORD UNIVERSITY · PI DUBRA, ALFREDO, GOLDBERG, JEFFREY L · 2017 to 2019
$1.1M
Interferometric Near-Infrared Spectroscopy for Tissue Flowmetry and OximetryR21NS105043 · NINDS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI SRINIVASAN, VIVEK JAY · 2017 to 2018
$432k
Improving penetration depth and spatial resolution for in vivo deep imaging of mouse brain using 2200 nm Optical Coherence MicroscopyR03EB023591 · NIBIB · UNIVERSITY OF CALIFORNIA AT DAVIS · PI SRINIVASAN, VIVEK JAY · 2017 to 2018
$157k
NEI NIH HHS R01 EY028287NIBIB NIH HHS R03 EB023591NINDS NIH HHS R01 NS094681NINDS NIH HHS R21 NS105043
6 · The paper itself

Abstract

In biological tissue, longer near-infrared wavelengths generally experience less scattering and more water absorption. Here we demonstrate an optical coherence tomography (OCT) system centered at 2.1 microns, whose bandwidth falls in the 2.2 micron water absorption optical window, for in vivo imaging of the rodent brain. We show in vivo that at 2.1 microns, the OCT signal is actually attenuated less in cranial bone than at 1.3 microns, and is also less susceptible to multiple scattering tails. We also show that the 2.2 micron window enables direct spectroscopic OCT assessment of tissue water content. We conclude that with further optimization, 2.2 micron OCT will have advantages in low-water-content tissue such as bone, as well as applications where extensive averaging is possible to compensate absorption losses.

Indexed as

AnimalsBrainImage Processing, Computer-AssistedRatsTomography, Optical CoherenceWaterWater

Identifiers

PMID31465349
PMCPMC7135935

What OpenQuestion holds

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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.