{"database":"biostudies-literature","file_versions":[],"scores":{"citationCount":0,"reanalysisCount":0,"viewCount":50,"searchCount":0},"additional":{"submitter":["Zhang Y"],"funding":["NIBIB NIH HHS","NICHD NIH HHS","Howard Hughes Medical Institute","NHLBI NIH HHS","NIAAA NIH HHS","U.S. Department of Health & Human Services | National Institutes of Health","National Science Foundation"],"pagination":["155"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8322159"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["10(1)"],"pubmed_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."],"journal":["Light, science & applications"],"pubmed_title":["Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data."],"pmcid":["PMC8322159"],"funding_grant_id":["R01 HL146745","R01 EB027099","R01 HD096335","R01 AA028406"],"pubmed_authors":["Cetintas E","Larin KV","Zhang Y","Singh M","Rivenson Y","Luo Y","Ozcan A","Liu T"],"view_count":["50"],"additional_accession":[]},"is_claimable":false,"name":"Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data.","description":"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.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Jul","modification":"2024-11-06T18:04:06.163Z","creation":"2022-02-11T02:01:51.389Z"},"accession":"S-EPMC8322159","cross_references":{"pubmed":["34326306"],"doi":["10.1038/s41377-021-00594-7"]}}