Evidence map›Paper›PMID 33520377›Full record

ArticleBiomedical optics express2021

Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising.

Lennart Husvogt, Stefan B Ploner, Siyu Chen, Daniel Stromer, Julia Schottenhamml, A Yasin Alibhai, Eric Moult, Nadia K Waheed, James G Fujimoto, Andreas Maier

Abstract read
In one paragraph

Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Advances in OCT Angiography.Translational vision science & technology · 2025
    Review
  2. Article
  3. Review
  4. 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.

Lennart HusvogtPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.ORCID https://orcid.org/0000-0002-7318-2680
Stefan B PlonerPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.ORCID https://orcid.org/0000-0003-4286-8687
Siyu ChenResearch Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Daniel StromerPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.
Julia SchottenhammlPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.
A Yasin AlibhaiNew England Eye Center, Tufts School of Medicine, Boston, MA 02111, USA.
Eric MoultResearch Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Nadia K WaheedNew England Eye Center, Tufts School of Medicine, Boston, MA 02111, USA.
James G FujimotoResearch Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Andreas MaierPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.

Funding

Novel Optical Diagnostics with Optical Coherence TomographyR01EY011289 · NEI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI JAMES G FUJIMOTO · 1995 to 2026
$9.5M
NEI NIH HHS R01 EY011289
6 · The paper itself

Abstract

Optical coherence tomography angiography (OCTA) is a novel and clinically promising imaging modality to image retinal and sub-retinal vasculature. Based on repeated optical coherence tomography (OCT) scans, intensity changes are observed over time and used to compute OCTA image data. OCTA data are prone to noise and artifacts caused by variations in flow speed and patient movement. We propose a novel iterative maximum a posteriori signal recovery algorithm in order to generate OCTA volumes with reduced noise and increased image quality. This algorithm is based on previous work on probabilistic OCTA signal models and maximum likelihood estimates. Reconstruction results using total variation minimization and wavelet shrinkage for regularization were compared against an OCTA ground truth volume, merged from six co-registered single OCTA volumes. The results show a significant improvement in peak signal-to-noise ratio and structural similarity. The presented algorithm brings together OCTA image generation and Bayesian statistics and can be developed into new OCTA image generation and denoising algorithms.

Identifiers

PMID33520377
PMCPMC7818957

What OpenQuestion holds

Textmetadata
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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.