Purpose

Elasticity of soft tissue provides valuable information to physicians during treatment and diagnosis of diseases. A number of approaches have been proposed to estimate tissue stiffness from the shear wave velocity. Optical coherence elastography offers a particularly high spatial and temporal resolution. However, current approaches typically acquire data at different positions sequentially, making it slow and less practical for clinical application.

Methods

We propose a new approach for elastography estimations using a fast imaging device to acquire small image volumes at rates of 831 Hz. The resulting sequence of phase image volumes is fed into a 4D convolutional neural network which handles both spatial and temporal data processing. We evaluate the approach on a set of image data acquired for gelatin phantoms of known elasticity.

Results

Using the neural network, the gelatin concentration of unseen samples was predicted with a mean error of 0.65 ± 0.81 percentage points from 90 subsequent volumes of phase data only. We achieve a data acquisition and data processing time of under 12 ms and 22 ms, respectively.

Conclusions

We demonstrate direct volumetric optical coherence elastography from phase image data. The approach does not rely on particular stimulation or sampling sequences and allows the estimation of elastic tissue properties of up to 40 Hz.

中文翻译：

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实时体积光学相干弹性成像的4D深度学习

目的

软组织的弹性为疾病的治疗和诊断过程中的医师提供了宝贵的信息。已经提出了许多方法来根据剪切波速度来估计组织刚度。光学相干弹性成像可提供特别高的空间和时间分辨率。然而，当前的方法通常顺序地在不同位置处获取数据，这使其速度变慢并且对于临床应用而言不太实用。

方法

我们提出一种使用快速成像设备进行弹性成像估计的新方法，以831 Hz的速率采集小图像。所得的相位图像体积序列被馈送到处理空间和时间数据处理的4D卷积神经网络。我们对获得已知弹性的明胶模型的图像数据集进行评估。

结果

使用神经网络，仅从90个后续体积的相数据中，预测未见样品的明胶浓度平均误差为0.65±0.81个百分点。我们分别实现了12 ms和22 ms以下的数据采集和数据处理时间。

结论

我们从相位图像数据演示直接体积光学相干弹性成像。该方法不依赖于特定的刺激或采样序列，并且允许估计高达40 Hz的弹性组织特性。