ArticleLight, science & applications2021
Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data.
Article in Light, science & applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- From intravascular imaging to adaptive vascular care: intelligent photonics and digital twins in panvascular disease.Light, science & applications · 2026Review
- Complex conjugate removal in optical coherence tomography using phase aware generative adversarial network.Journal of biomedical optics · 2025Article
- Reconstruction of Optical Coherence Tomography Images from Wavelength Space Using Deep Learning.Sensors (Basel, Switzerland) · 2024Article
- Physics-guided deep learning-based real-time image reconstruction of Fourier-domain optical coherence tomography.Biomedical optics express · 2024Article
- Neural network-based processing and reconstruction of compromised biophotonic image data.Light, science & applications · 2024Review
- Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine.Journal of personalized medicine · 2023Review
- Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network.Biomedical optics express · 2023Article
- Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe.Communications biology · 2023Article
- SiamHAS: Siamese Tracker with Hierarchical Attention Strategy for Aerial Tracking.Micromachines · 2023Article
- Multi-Scale Reconstruction of Undersampled Spectral-Spatial OCT Data for Coronary Imaging Using Deep Learning.IEEE transactions on bio-medical engineering · 2022Article
- Light People: Professor Aydogan Ozcan.Light, science & applications · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Optical coherence tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical setup and can be easily integrated with existing swept-source or spectral-domain OCT systems to reduce the amount of raw spectral data to be acquired. To show the efficacy of this framework, we trained and blindly tested a deep neural network using mouse embryo samples imaged by an SS-OCT system. Using 2-fold undersampled spectral data (i.e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in 0.59 ms using multiple graphics-processing units (GPUs), removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i.e., 1280 spectral points per A-line). We also successfully demonstrate that this framework can be further extended to process 3× undersampled spectral data per A-line, with some performance degradation in the reconstructed image quality compared to 2× spectral undersampling. Furthermore, an A-line-optimized undersampling method is presented by jointly optimizing the spectral sampling locations and the corresponding image reconstruction network, which improved the overall imaging performance using less spectral data points per A-line compared to 2× or 3× spectral undersampling results. This deep learning-enabled image reconstruction approach can be broadly used in various forms of spectral-domain OCT systems, helping to increase their imaging speed without sacrificing image resolution and signal-to-noise ratio.
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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.