Contrast enhanced ultrasound is a radiation-free imaging modality which uses encapsulated gas microbubbles for improved visualization of the vascular bed deep within the tissue. It has recently been used to enable imaging with unprecedented subwavelength spatial resolution by relying on super-resolution techniques. A typical preprocessing step in super-resolution ultrasound is to separate the microbubble signal from the cluttering tissue signal. This step has a crucial impact on the final image quality. Here, we propose a new approach to clutter removal based on robust principle component analysis (PCA) and deep learning. We begin by modeling the acquired contrast enhanced ultrasound signal as a combination of low rank and sparse components. This model is used in robust PCA and was previously suggested in the context of ultrasound Doppler processing and dynamic magnetic resonance imaging. We then illustrate that an iterative algorithm based on this model exhibits improved separation of microbubble signal from the tissue signal over commonly practiced methods. Next, we apply the concept of deep unfolding to suggest a deep network architecture tailored to our clutter filtering problem which exhibits improved convergence speed and accuracy with respect to its iterative counterpart. We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and with the fast iterative shrinkage algorithm. We show that our architecture exhibits better image quality and contrast.

中文翻译：

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深度展开的稳健PCA及其在超声中抑制杂波的应用。

对比增强超声是一种无辐射的成像方式，它使用封装的气体微气泡来改善组织深处的血管床的可视化。最近，它已被用来依靠超分辨率技术以前所未有的亚波长空间分辨率进行成像。超分辨率超声中的典型预处理步骤是将微泡信号与混乱的组织信号分离。此步骤对最终图像质量有至关重要的影响。在这里，我们提出了一种基于鲁棒主成分分析（PCA）和深度学习的杂波去除新方法。我们首先将获取的对比度增强超声信号建模为低秩和稀疏分量的组合。该模型用于鲁棒的PCA中，先前已在超声多普勒处理和动态磁共振成像的背景下提出。然后，我们说明了一种基于该模型的迭代算法，与常规方法相比，该方法展现出了微气泡信号与组织信号的改进分离。接下来，我们应用深度展开的概念来建议针对我们的杂波过滤问题量身定制的深度网络体系结构，该体系结构相对于其迭代对应物具有更高的收敛速度和准确性。我们将建议的深层网络在仿真和体内大鼠脑部扫描中的性能与常用的深层网络体系结构和快速迭代收缩算法进行比较。我们证明了我们的架构表现出更好的图像质量和对比度。