Evidence map›Paper›PMID 29522050›Full record

ArticleJournal of the Optical Society of America. A, Optics, image science, and vision2018

Wavefront measurement using computational adaptive optics.

Fredrick A South, Yuan-Zhi Liu, Andrew J Bower, Yang Xu, P Scott Carney, Stephen A Boppart

Abstract read
In one paragraph

Article in Journal of the Optical Society of America. A, Optics, image science, and vision, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Adaptive optics for optical microscopy [Invited].Biomedical optics express · 2023
    Review
  6. Article
  7. Article
  8. Article
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  14. Analysis of Damage and Wound Healing in the Retinal Pigmented Epithelium.Advances in experimental medicine and biology · 2019
    Article
  15. 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

6 authors.

Fredrick A South
Yuan-Zhi Liu
Andrew J Bower
Yang Xu
P Scott Carney
Stephen A Boppart

Funding

Partnership for Primary Care Imaging of the EarR01EB013723 · NIBIB · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI BOPPART, STEPHEN A · 2011 to 2018
$6.4M
Intraoperative Polarization-Sensitive OCT for Assessing Breast Tumor MarginsR01CA213149 · NCI · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI BOPPART, STEPHEN A · 2017 to 2021
$2.4M
Optical Amplification Microscopy of Weak Back-Scattered LightR01EB023232 · NIBIB · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI BOPPART, STEPHEN A · 2016 to 2019
$1.8M
NCI NIH HHS R01 CA213149NIBIB NIH HHS R01 EB013723NIBIB NIH HHS R01 EB023232
6 · The paper itself

Abstract

In many optical imaging applications, it is necessary to correct for aberrations to obtain high quality images. Optical coherence tomography (OCT) provides access to the amplitude and phase of the backscattered optical field for three-dimensional (3D) imaging samples. Computational adaptive optics (CAO) modifies the phase of the OCT data in the spatial frequency domain to correct optical aberrations without using a deformable mirror, as is commonly done in hardware-based adaptive optics (AO). This provides improvement of image quality throughout the 3D volume, enabling imaging across greater depth ranges and in highly aberrated samples. However, the CAO aberration correction has a complicated relation to the imaging pupil and is not a direct measurement of the pupil aberrations. Here we present new methods for recovering the wavefront aberrations directly from the OCT data without the use of hardware adaptive optics. This enables both computational measurement and correction of optical aberrations.

Identifiers

PMID29522050
PMCPMC5915320

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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.