Evidence map›Paper›PMID 32986562›Full record

ArticleIEEE journal of biomedical and health informatics2020

3D Retinal Vessel Density Mapping With OCT-Angiography.

Mona Sharifi Sarabi, Maziyar M Khansari, Jiong Zhang, Sam Kushner-Lenhoff, Jin Kyu Gahm, Yuchuan Qiao, Amir H Kashani, Yonggang Shi

Open access · greenAbstract read
In one paragraph

Article in IEEE journal of biomedical and health informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 19% 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

  1. Pooled it
  2. Strip-Scanning for Efficient Widefield Retinal OCTA Mosaicking.Translational vision science & technology · 2026
    Article
  3. Article
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  6. Review
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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

8 authors at 2 institutions in 2 countries.

Mona Sharifi Sarabi
Maziyar M Khansari
Jiong Zhang
Sam Kushner-Lenhoff
Jin Kyu Gahm
Yuchuan Qiao
Amir H Kashani
Yonggang Shi
University of Southern California · USPusan National University · KR

Funding

TR&D3: Intrinsic Surface MappingP41EB015922 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M, TOGA, ARTHUR W · 2012 to 2022
$14.1M
Imaging Cerebral and Retinal Microvasculature in Cerebral Small Vessel DiseaseUH3NS100614 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI KASHANI, AMIR H, RINGMAN, JOHN M · 2018 to 2020
$4.3M
Human Connectomes for Low Vision, Blindness, and Sight RestorationU01EY025864 · NEI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI AGUIRRE, GEOFFREY KARL, PATEL, VIVEK R. · 2015 to 2018
$4.0M
Assessment of Retinal Capillary Density, Morphology and Function in Retinal Vascular Disease Using Novel OCT Angiography Based MetricsR01EY030564 · NEI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI KASHANI, AMIR H · 2019 to 2024
$3.5M
Imaging Cerebral and Retinal Microvasculature in Cerebral Small Vessel DiseaseUH2NS100614 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI KASHANI, AMIR H, RINGMAN, JOHN M · 2016 to 2017
$2.4M
Functional Imaging in Hypoxic-Ischemic Retinal DiseaseK08EY027006 · NEI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI KASHANI, AMIR H · 2016 to 2020
$978k
3D OCT Angiography for quantitative characterization of diabetic retinopathyR21EY027879 · NEI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI KASHANI, AMIR H, SHI, YONGGANG · 2017 to 2018
$454k
NEI NIH HHS K08 EY027006NEI NIH HHS R01 EY030564NEI NIH HHS R21 EY027879NEI NIH HHS U01 EY025864NIBIB NIH HHS P41 EB015922NINDS NIH HHS UH2 NS100614NINDS NIH HHS UH3 NS100614
6 · The paper itself

Abstract

Optical Coherence Tomography Angiography (OCTA) is a novel, non-invasive imaging modality of retinal capillaries at micron resolution. Recent studies have correlated macular OCTA vascular measures with retinal disease severity and supported their use as a diagnostic tool. However, these measurements mostly rely on a few summary statistics in retinal layers or regions of interest in the two-dimensional (2D) en face projection images. To enable 3D and localized comparisons of retinal vasculature between longitudinal scans and across populations, we develop a novel approach for mapping retinal vessel density from OCTA images. We first obtain a high-quality 3D representation of OCTA-based vessel networks via curvelet-based denoising and optimally oriented flux (OOF). Then, an effective 3D retinal vessel density mapping method is proposed. In this framework, a vessel density image (VDI) is constructed by diffusing the vessel mask derived from OOF-based analysis to the entire image volume. Subsequently, we utilize a non-linear, 3D OCT image registration method to provide localized comparisons of retinal vasculature across subjects. In our experimental results, we demonstrate an application of our method for longitudinal qualitative analysis of two pathological subjects with edema during the course of clinical care. Additionally, we quantitatively validate our method on synthetic data with simulated capillary dropout, a dataset obtained from a normal control (NC) population divided into two age groups and a dataset obtained from patients with diabetic retinopathy (DR). Our results show that we can successfully detect localized vascular changes caused by simulated capillary loss, normal aging, and DR pathology even in presence of edema. These results demonstrate the potential of the proposed framework in localized detection of microvascular changes and monitoring retinal disease progression.

Indexed as

AngiographyDiabetic RetinopathyHumansImage Interpretation, Computer-AssistedImaging, Three-DimensionalRetinal VesselsTomography, Optical Coherence

Identifiers

PMID32986562
PMCPMC7737654
OpenAlexW3085288041

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.