Evidence map›Paper›PMID 35386729›Full record

ArticleNature photonics2019

Optical coherence refraction tomography.

Kevin C Zhou, Ruobing Qian, Simone Degan, Sina Farsiu, Joseph A Izatt

Abstract read
In one paragraph

Article in Nature photonics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

  1. Article
  2. Volumetric montaging of optical coherence tomography in human retinas.bioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Visualization of CONature chemistry · 2024
    Article
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  11. Review
  12. Article
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  18. Review
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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

5 authors.

Kevin C ZhouDepartment of Biomedical Engineering, Duke University, Durham, NC 27708.
Ruobing QianDepartment of Biomedical Engineering, Duke University, Durham, NC 27708.
Simone DeganDepartment of Radiology, Duke University Medical Center, Durham, NC 27708.
Sina FarsiuDepartment of Biomedical Engineering, Duke University, Durham, NC 27708.
Joseph A IzattDepartment of Biomedical Engineering, Duke University, Durham, NC 27708.

Funding

VISION RESEARCHP30EY005722 · NEI · DUKE UNIVERSITY · PI Vadim Y Arshavsky · 1985 to 2026
$19.3M
Intraoperative OCT Guidance of Intraocular SurgeryU01EY028079 · NEI · DUKE UNIVERSITY · PI KUO, ANTHONY NANLIN, TOTH, CYNTHIA ANN · 2018 to 2022
$3.8M
NEI NIH HHS P30 EY005722NEI NIH HHS U01 EY028079
6 · The paper itself

Abstract

Optical coherence tomography (OCT) is a cross-sectional, micron-scale imaging modality with widespread clinical application. Typical OCT systems sacrifice lateral resolution to achieve long depths of focus for bulk tissue imaging, and hence tend to have better axial than lateral resolution. Such anisotropic resolution can obscure fine ultrastructural features. Furthermore, conventional OCT suffers from refraction-induced image distortions. Here, we introduce optical coherence refraction tomography (OCRT), which extends the superior axial resolution to the lateral dimension, synthesising undistorted cross-sectional image reconstructions from multiple conventional images acquired with angular diversity. In correcting refraction-induced distortions to register the OCT images, OCRT also achieves spatially resolved refractive index imaging. We demonstrate >3-fold improvement in lateral resolution as well as speckle reduction in imaging tissue ultrastructure, consistent with histology. With further optimisation in optical designs to incorporate angular diversity into clinical instruments, OCRT could be widely applied as an enhancement over conventional OCT.

Identifiers

PMID35386729
PMCPMC8982998

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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.