Evidence map›Paper›PMID 34222553›Full record

ArticlePhotonics research2020

Wide-field Ophthalmic Space-Division Multiplexing Optical Coherence Tomography.

Jason Jerwick, Yongyang Huang, Zhao Dong, Adrienne Slaudades, Alexander J Brucker, Chao Zhou

Open access · greenAbstract read
In one paragraph

Article in Photonics research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 35% of its field
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

5 citing papers in PubMed, 17 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Jason JerwickDepartment of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA.
Yongyang HuangDepartment of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA.
Zhao DongDepartment of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA.
Adrienne SlaudadesScheie Eye Institute, Penn Presbyterian Medical Center, University of Pennsylvania, Philadelphia PA, 19104.
Alexander J BruckerScheie Eye Institute, Penn Presbyterian Medical Center, University of Pennsylvania, Philadelphia PA, 19104.
Chao ZhouDepartment of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA.
Lehigh University · USPenn Presbyterian Medical Center · US

Funding

High throughput optical coherence tomography (OCT)-based imaging platform for label-free, non-invasive characterization of 3D tumor spheroids.R01EB025209 · NIBIB · WASHINGTON UNIVERSITY · PI ZHOU, CHAO · 2017 to 2021
$1.4M
NIBIB NIH HHS R01 EB025209
6 · The paper itself

Abstract

High-speed ophthalmic optical coherence tomography systems are of interest because they allow rapid, motion-free, and wide-field retinal imaging. Space-division multiplexing optical coherence tomography (SDM-OCT) is a high-speed imaging technology which takes advantage of the long coherence length of microelectromechanical vertical cavity surface emitting laser (MEMs VCSEL) sources to multiplex multiple images along a single imaging depth. We demonstrate wide-field retinal OCT imaging, acquired at an effective A-scan rate of 800,000 A-scans/sec with volumetric images covering up to 12.5 mm × 7.4 mm on the retina acquired in less than 1 second. A clinical feasibility study was conducted to compare the ophthalmic SDM-OCT with commercial OCT systems, illustrating the high-speed capability of SDM-OCT in a clinical setting.

Indexed as

High Speed ImagingOphthalmologyOptical Coherence Tomography

Identifiers

PMID34222553
PMCPMC8248931
OpenAlexW3001605188

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.