Evidence map›Paper›PMID 32058481›Full record

ArticleOptics letters2020

Extended imaging depth of en-face optical coherence tomography based on fast measurement of a reflection matrix by wide-field heterodyne detection.

Qiang Yang, Jing Cao, Yusi Miao, Jiang Zhu, Zhongping Chen

Abstract read
In one paragraph

Article in Optics letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

5 authors.

Qiang Yang
Jing Cao
Yusi Miao
Jiang Zhu
Zhongping Chen

Funding

Phase resolved ARF optical coherence elastography for intravascular imagingR01HL125084 · NHLBI · UNIVERSITY OF CALIFORNIA-IRVINE · PI CHEN, ZHONGPING, ZHOU, QIFA · 2014 to 2023
$5.6M
Combined OCT/US/PAT system for intravascular ImagingR01HL127271 · NHLBI · UNIVERSITY OF CALIFORNIA-IRVINE · PI CHEN, ZHONGPING, PATEL, PRANAV · 2015 to 2018
$2.7M
High-resolution Elastographic Assessment of the Optic Nerve HeadR01EY028662 · NEI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI CHEN, ZHONGPING, SIGAL, IAN A · 2018 to 2021
$2.7M
High Resolution Elastograpy of Retina Under Prosthetic Electrical StimulationR01EY026091 · NEI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI CHEN, ZHONGPING, ZHOU, QIFA · 2016 to 2019
$2.0M
NEI NIH HHS R01 EY026091NEI NIH HHS R01 EY028662NHLBI NIH HHS R01 HL125084NHLBI NIH HHS R01 HL127271
6 · The paper itself

Abstract

Multiple light scattering in biomedical tissue limits the penetration depth of optical imaging systems such as optical coherence tomography. To increase the imaging depth in scattering media, a computational method based on coherent reflection matrix measurement has been developed using low coherence interferometry. The complex reflection matrix is obtained via point-by-point scanning followed by a phase-shifting method; then singular value decomposition is used to retrieve the singly back-scattered light. However, the in vivo application of the current reported method is limited due to the slow acquisition speed of the matrix. In this Letter, a wide-field heterodyne-detection method is adopted to speed up the complex matrix measurement at a deep tissue layer. Compared to the phase-shifting method, the heterodyne-detection scheme retrieves depth-resolved complex amplitudes faster and is more stable without mechanical movement of the reference mirror. As a result, the matrix measurement speed is increased by more than one order of magnitude.

Indexed as

Image Processing, Computer-AssistedInterferometryOptical PhenomenaSignal-To-Noise RatioTomography, Optical Coherence

Identifiers

PMID32058481
PMCPMC7735219

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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.