Deep-learning-based approach for strain estimation in phase-sensitive optical coherence elastography

In this Letter, a deep-learning-based approach is proposed for estimating the strain field distributions in phase-sensitive optical coherence elastography. The method first uses the simulated wrapped phase maps and corresponding phase-gradient maps to train the strain estimation convolution neural n...

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Bibliographic Details
Published in:Optics letters 2021-12, Vol.46 (23), p.5914-5917
Main Authors: Dong, Bo, Huang, Naixing, Bai, Yulei, Xie, Shengli
Format: Article
Language:English
Subjects:
Artificial neural networks
Coherence
Deep learning
Estimation
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Summary:In this Letter, a deep-learning-based approach is proposed for estimating the strain field distributions in phase-sensitive optical coherence elastography. The method first uses the simulated wrapped phase maps and corresponding phase-gradient maps to train the strain estimation convolution neural network (CNN) and then employs the trained CNN to calculate the strain fields from measured phase-difference maps. Two specimens with different deformations, one with homogeneous and the other with heterogeneous, were measured for validation. The strain field distributions of the specimens estimated by different approaches were compared. The results indicate that the proposed deep-learning-based approach features much better performance than the popular vector method, enhancing the SNR of the strain results by 21.6 dB.
ISSN:0146-9592
1539-4794
DOI:10.1364/OL.446403