IEEE Trans Med Imaging. 2022 Apr;41(4):915-924. doi: 10.1109/TMI.2021.3128641. Epub 2022 Apr 1.
Imaging applications tailored towards ultrasound-based treatment, such as high intensity focused ultrasound (FUS), where higher power ultrasound generates a radiation force for ultrasound elasticity imaging or therapeutics/theranostics, are affected by interference from FUS. The artifact becomes more pronounced with intensity and power. To overcome this limitation, we propose FUS-net, a method that incorporates a CNN-based U-net autoencoder trained end-to-end on 'clean' and 'corrupted' RF data in Tensorflow 2.3 for FUS artifact removal. The network learns the representation of RF data and FUS artifacts in latent space so that the output of corrupted RF input is clean RF data. We find that FUS-net perform 15% better than stacked autoencoders (SAE) on evaluated test datasets. B-mode images beamformed from FUS-net RF shows superior speckle quality and better contrast-to-noise (CNR) than both notch-filtered and adaptive least means squares filtered RF data. Furthermore, FUS-net filtered images had lower errors and higher similarity to clean images collected from unseen scans at all pressure levels. Lastly, FUS-net RF can be used with existing cross-correlation speckle-tracking algorithms to generate displacement maps. FUS-net currently outperforms conventional filtering and SAEs for removing high pressure FUS interference from RF data, and hence may be applicable to all FUS-based imaging and therapeutic methods.
针对基于超声的治疗的成像应用，如高强度聚焦超声（FUS），其中更高的功率超声产生辐射力用于超声弹性成像或治疗/治疗学，受到 FUS 的干扰。随着强度和功率的增加，伪影变得更加明显。为了克服这一限制，我们提出了 FUS-net，这是一种方法，它在 Tensorflow 2.3 中基于 CNN 的 U-net 自动编码器对“干净”和“损坏”RF 数据进行端到端训练，用于 FUS 伪影去除。该网络学习 RF 数据和 FUS 伪影在潜在空间中的表示，以便损坏的 RF 输入的输出是干净的 RF 数据。我们发现，在评估的测试数据集上，FUS-net 的性能比堆叠自动编码器（SAE）好 15%。从 FUS-net RF 生成的 B 模式图像具有更好的斑点质量和更高的对比度噪声比（CNR），优于 notch 滤波和自适应最小均方滤波的 RF 数据。此外，在所有压力水平下，FUS-net 滤波后的图像错误更低，与从未见扫描中收集的干净图像更相似。最后，FUS-net RF 可以与现有的互相关斑点跟踪算法一起使用，以生成位移图。FUS-net 目前在从 RF 数据中去除高压 FUS 干扰方面优于传统滤波和 SAE，因此可能适用于所有基于 FUS 的成像和治疗方法。
