Evidence map›Paper›PMID 37342716›Full record

ArticleBiomedical optics express2023

MAS-Net OCT: a deep-learning-based speckle-free multiple aperture synthetic optical coherence tomography.

Renxiong Wu, Shaoyan Huang, Junming Zhong, Meixuan Li, Fei Zheng, En Bo, Linbo Liu, Yong Liu, Xin Ge, Guangming Ni

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical optics express, 2023. 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
1.0field-weighted citation impact, top 28% 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

2 citing papers in PubMed, 9 citations in OpenAlex.

  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

10 authors at 3 institutions in 2 countries.

Renxiong WuSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Shaoyan HuangSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Junming ZhongSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Meixuan LiSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Fei ZhengSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
En BoSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Linbo LiuSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Yong LiuSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Xin GeSchool of Science, Shenzhen Campus of Sun Yat-sen University, Shenzhen 510275, China.
Guangming NiSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.ORCID https://orcid.org/0000-0002-4516-256X
University of Electronic Science and Technology of China · CNNanyang Technological University · SGSun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-resolution spectral domain optical coherence tomography (SD-OCT) is a vital clinical technique that suffers from the inherent compromise between transverse resolution and depth of focus (DOF). Meanwhile, speckle noise worsens OCT imaging resolving power and restricts potential resolution-enhancement techniques. Multiple aperture synthetic (MAS) OCT transmits light signals and records sample echoes along a synthetic aperture to extend DOF, acquired by time-encoding or optical path length encoding. In this work, a deep-learning-based multiple aperture synthetic OCT termed MAS-Net OCT, which integrated a speckle-free model based on self-supervised learning, was proposed. MAS-Net was trained on datasets generated by the MAS OCT system. Here we performed experiments on homemade microparticle samples and various biological tissues. Results demonstrated that the proposed MAS-Net OCT could effectively improve the transverse resolution in a large imaging depth as well as reduced most speckle noise.

Identifiers

PMID37342716
PMCPMC10278634
OpenAlexW4367853970

What OpenQuestion holds

Textmetadata
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.