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Cited 12 time in webofscience Cited 12 time in scopus
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Integrated deep learning framework for accelerated optical coherence tomography angiography SCIE SCOPUS

Title
Integrated deep learning framework for accelerated optical coherence tomography angiography
Authors
Kim, GyuwonKim, JongbeomChoi, Woo JuneKim, ChulhongLee, Seungchul
Date Issued
2022-01
Publisher
Nature Publishing Group
Abstract
AbstractLabel-free optical coherence tomography angiography (OCTA) has become a premium imaging tool in clinics to obtain structural and functional information of microvasculatures. One primary technical drawback for OCTA, however, is its imaging speed. The current protocols require high sampling density and multiple acquisitions of cross-sectional B-scans to form one image frame, resulting in low acquisition speed. Recently, deep learning (DL)-based methods have gained attention in accelerating the OCTA acquisition process. They achieve faster acquisition using two independent reconstructing approaches: high-quality angiograms from a few repeated B-scans and high-resolution angiograms from undersampled data. While these approaches have shown promising results, they provide limited solutions that only partially account for the OCTA scanning mechanism. Herein, we propose an integrated DL method to simultaneously tackle both factors and further enhance the reconstruction performance in speed and quality. We designed an end-to-end deep neural network (DNN) framework with a two-staged adversarial training scheme to reconstruct fully-sampled, high-quality (8 repeated B-scans) angiograms from their corresponding undersampled, low-quality (2 repeated B-scans) counterparts by successively enhancing the pixel resolution and the image quality. Using an in-vivo mouse brain vasculature dataset, we evaluate our proposed framework through quantitative and qualitative assessments and demonstrate that our method can achieve superior reconstruction performance compared to the conventional means. Our DL-based framework can accelerate the OCTA imaging speed from 16 to 256$$\times $$ × while preserving the image quality, thus enabling a convenient software-only solution to enhance preclinical and clinical studies.
URI
https://oasis.postech.ac.kr/handle/2014.oak/109358
DOI
10.1038/s41598-022-05281-0
ISSN
2045-2322
Article Type
Article
Citation
Scientific Reports, vol. 12, no. 1, 2022-01
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이승철LEE, SEUNGCHUL
Dept of Mechanical Enginrg
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