Evidence map›Paper›PMID 30319912›Full record

ArticleBiomedical optics express2018

Aberration-diverse optical coherence tomography for suppression of multiple scattering and speckle.

Siyang Liu, Michael R E Lamont, Jeffrey A Mulligan, Steven G Adie

Abstract read
In one paragraph

Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Multiple scattering suppression forBiomedical optics express · 2024
    Article
  2. Article
  3. Article
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  9. Crosstalk-free volumetricBiomedical optics express · 2019
    Article
  10. Article
  11. 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.

Siyang LiuSchool of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA.
Michael R E LamontNancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USA.
Jeffrey A MulliganSchool of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA.
Steven G AdieNancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USA.

Funding

Development of Hybrid Adaptive Optics for Multimodal Microscopy Deep in the Mouse BrainR21EY028389 · NEI · CORNELL UNIVERSITY · PI ADIE, STEVEN GRAHAM · 2017 to 2018
$491k
Volumetric Traction Force Tomography of Collective Cell Migration DynamicsR21EB022927 · NIBIB · CORNELL UNIVERSITY · PI ADIE, STEVEN GRAHAM · 2016 to 2017
$434k
NEI NIH HHS R21 EY028389NIBIB NIH HHS R21 EB022927
6 · The paper itself

Abstract

Multiple scattering is a major barrier that limits the optical imaging depth in scattering media. In order to alleviate this effect, we demonstrate aberration-diverse optical coherence tomography (AD-OCT), which exploits the phase correlation between the deterministic signals from single-scattered photons to suppress the random background caused by multiple scattering and speckle. AD-OCT illuminates the sample volume with diverse aberrated point spread functions, and computationally removes these intentionally applied aberrations. After accumulating 12 astigmatism-diverse OCT volumes, we show a 10 dB enhancement in signal-to-background ratio via a coherent average of reconstructed signals from a USAF target located 7.2 scattering mean free paths below a thick scattering layer, and a 3× speckle contrast reduction from an incoherent average of reconstructed signals inside the scattering layer. This AD-OCT method, when implemented using astigmatic illumination, is a promising approach for ultra-deep volumetric optical coherence microscopy.

Identifiers

PMID30319912
PMCPMC6179412

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Registered trials

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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.