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.

中文翻译：

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基于深度学习的相敏光学相干弹性成像应变估计方法

在这封信中，提出了一种基于深度学习的方法来估计相敏光学相干弹性成像中的应变场分布。该方法首先使用模拟的包裹相位图和相应的相位梯度图来训练应变估计卷积神经网络 (CNN)，然后使用训练后的 CNN 从测量的相位差图中计算应变场。测量了具有不同变形的两个样品，一个具有均质性，另一个具有异质性，以进行验证。比较了通过不同方法估计的试样的应变场分布。结果表明，所提出的基于深度学习的方法的性能比流行的矢量方法要好得多，应变结果的 SNR 提高了 21.6 dB。