Purpose

We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic reconstruction to form, as compared to common reconstructions constrained by physical models using fixed speed of sound assumptions.

Methods

We propose a U-Net-like architecture for the given task. Additional layers with strided convolutions downsample the raw data. Hyperparameter optimization was used to find a suitable learning rate. We train and test our deep learning approach on plane wave ultrasound images with a single insonification angle. The dataset includes phantom as well as in vivo data.

Results

The images produced by our method are visually comparable to ones reconstructed with the conventional delay and sum algorithm. Deviations between prediction and ground truth are likely to be related to speckle noise. For the test set, the mean absolute error is \(4.23 \pm 1.52\) for the phantom images and \(6.09 \pm 0.72\) for the in vivo data.

Conclusion

The result shows the feasibility of our approach and opens up new research directions regarding information retrieval from raw channel data. As the networks reconstruction performance is limited by the quality of the ground truth images, using other ultrasound reconstruction technique or image types as target data would be of interest.

中文翻译：

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基于深度学习从原始通道数据重建超声图像。

目的

我们研究了使用深度学习网络直接从原始通道数据重建超声图像的可行性。从原始数据开始，我们向网络呈现完整的测量信息，与使用固定声速假设的物理模型约束的常见重建相比，允许形成更通用的重建。

方法

我们为给定的任务提出了一个类似 U-Net 的架构。具有跨步卷积的附加层对原始数据进行下采样。超参数优化用于找到合适的学习率。我们在具有单个声波角度的平面波超声图像上训练和测试我们的深度学习方法。该数据集包括体模和体内数据。

结果

我们的方法产生的图像在视觉上与使用传统延迟和求和算法重建的图像相当。预测和真实情况之间的偏差可能与散斑噪声有关。对于测试集，体模图像的平均绝对误差为\(4.23 \pm 1.52\)，体内数据的平均绝对误差为\(6.09 \pm 0.72\)。

结论

结果表明了我们方法的可行性，并开辟了有关从原始通道数据中检索信息的新研究方向。由于网络重建性能受地面实况图像质量的限制，因此使用其他超声重建技术或图像类型作为目标数据会很有趣。