  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 a 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 the fast iterative shrinkage algorithm, and show that our architecture
exhibits better image quality and contrast.
