ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2020
Super-resolution technology to simultaneously improve optical & digital resolution of optical coherence tomography via deep learning.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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.
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.
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Who cites it
12 citing papers in PubMed, 18 citations in OpenAlex.
- Volumetric Imaging of Ex Vivo Oral Mucosa Specimens with Multi-Scale Wide Field-of-View Optical Coherence Tomography/Microscopy in Near-Infrared-II Window.Diagnostics (Basel, Switzerland) · 2026Article
- Automated cell properties toolbox from 3D bioprinted hydrogel scaffolds via deep learning and optical coherence tomography.Biomedical optics express · 2025Article
- Deep-learning-driven optical coherence tomography analysis for cardiovascular outcome prediction in patients with acute coronary syndrome.European heart journal. Digital health · 2024Article
- High-resolutionBiomedical optics express · 2024Article
- PSCAT: a lightweight transformer for simultaneous denoising and super-resolution of OCT images.Biomedical optics express · 2024Article
- Rigid alignment method for secondary analyses of optical coherence tomography volumes.Biomedical optics express · 2024Article
- Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe.Communications biology · 2023Article
- Toward reliable calcification detection: calibration of uncertainty in object detection from coronary optical coherence tomography images.Journal of biomedical optics · 2023Article
- Multi-Scale Reconstruction of Undersampled Spectral-Spatial OCT Data for Coronary Imaging Using Deep Learning.IEEE transactions on bio-medical engineering · 2022Article
- A review of generative adversarial network applications in optical coherence tomography image analysis.Journal of optometry · 2022Review
- Reconstruction of visible light optical coherence tomography images retrieved from discontinuous spectral data using a conditional generative adversarial network.Biomedical optics express · 2021Article
- Inpainting for Saturation Artifacts in Optical Coherence Tomography Using Dictionary-Based Sparse Representation.IEEE photonics journal · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
Abstract
Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment. In cardiac imaging, OCT has been used in assessing plaques before and after stenting. While needed in many scenarios, high resolution comes at the costs of demanding optical design and data storage/transmission. In OCT, there are two types of resolutions to characterize image quality: optical and digital resolutions. Although multiple existing works have heavily emphasized on improving the digital resolution, the studies on improving optical resolution or both resolutions remain scarce. In this paper, we focus on improving both resolutions. In particular, we investigate a deep learning method to address the problem of generating a high-resolution (HR) OCT image from a low optical and low digital resolution (L
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Registered trials
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.