Physics-guided deep-learning-based image reconstruction for Fourier-domain optical coherence tomography

Mengyuan Wang; Yuye Ling; Yikai Su

doi:10.1117/12.3005433

13 March 2024 Physics-guided deep-learning-based image reconstruction for Fourier-domain optical coherence tomography

Mengyuan Wang, Yuye Ling, Yikai Su

Proceedings Volume PC12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII; PC1283019 (2024) https://doi.org/10.1117/12.3005433
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

In Fourier-domain optical coherence tomography (FD-OCT), image reconstruction has been extensively studied. This paper addresses the trade-off between reconstruction time and image quality of the optimization methods by proposing an unsupervised deep learning-based approach. Different from the existing learning-based methods, the proposed unsupervised learning method incorporates a neural network as an inverse solver and eliminates the need for large training pairs. A proof-of-concept simulation was conducted, comparing our method with an iterative optimization technique using stochastic gradient descent (SGD). Results show that the proposed method achieves real-time reconstruction with a small decrease in image quality compared to SGD, while enabling real-time reconstruction at a speed of 0.008s per B-scan (125 frames per second). In contrast, the SGD method took 0.32s per B-scan, making it 40 times slower. This deep learning-based method has significant potential for real-time image reconstruction and display in future FD-OCT.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mengyuan Wang, Yuye Ling, and Yikai Su "Physics-guided deep-learning-based image reconstruction for Fourier-domain optical coherence tomography", Proc. SPIE PC12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII, PC1283019 (13 March 2024); https://doi.org/10.1117/12.3005433

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