论文标题

深度超声DENOSING,没有干净的数据

Deep Ultrasound Denoising Without Clean Data

论文作者

Goudarzi, Sobhan, Rivaz, Hassan

论文摘要

一方面,随着通过组织传播时,传输的超声梁被减弱。另一方面,接收到的射频(RF)数据包含一个加性高斯噪声,该噪声由采集卡和传感器噪声带来。这两个因素导致RF数据中的信号与噪声比(SNR)的降低,有效地使B模式图像的深区域高度不可靠。有三种常见的方法来减轻此问题。首先,增加受安全阈值限制的传输光束的功率。平均连续帧是第二个选项,不仅可以减少帧率,而且不适用于移动目标。第三,降低了传输频率,从而恶化了空间分辨率。已经开发了许多深层的Denoising技术,但是它们通常需要干净的数据来训练模型,这通常仅在模拟图像中可用。在此,提出了不需要干净的训练目标的深层降噪方法。该模型是在嘈杂的输入输出对之间构建的,训练过程有趣地收敛于嘈杂对的平均值。关于实际幻影以及离体数据的实验结果证实了提出的消除噪声方法的功效。

On one hand, the transmitted ultrasound beam gets attenuated as propagates through the tissue. On the other hand, the received Radio-Frequency (RF) data contains an additive Gaussian noise which is brought about by the acquisition card and the sensor noise. These two factors lead to a decreasing Signal to Noise Ratio (SNR) in the RF data with depth, effectively rendering deep regions of B-Mode images highly unreliable. There are three common approaches to mitigate this problem. First, increasing the power of transmitted beam which is limited by safety threshold. Averaging consecutive frames is the second option which not only reduces the framerate but also is not applicable for moving targets. And third, reducing the transmission frequency, which deteriorates spatial resolution. Many deep denoising techniques have been developed, but they often require clean data for training the model, which is usually only available in simulated images. Herein, a deep noise reduction approach is proposed which does not need clean training target. The model is constructed between noisy input-output pairs, and the training process interestingly converges to the clean image that is the average of noisy pairs. Experimental results on real phantom as well as ex vivo data confirm the efficacy of the proposed method for noise cancellation.

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