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Contrast enhanced ultrasound is a radiation-free imaging modality which usesencapsulated gas microbubbles for improved visualization of the vascular beddeep within the tissue. It has recently been used to enable imaging withunprecedented subwavelength spatial resolution by relying on super-resolutiontechniques. A typical preprocessing step in super-resolution ultrasound is toseparate the microbubble signal from the cluttering tissue signal. This stephas a crucial impact on the final image quality. Here, we propose a newapproach to clutter removal based on robust principle component analysis (PCA)and deep learning. We begin by modeling the acquired contrast enhancedultrasound signal as a combination of a low rank and sparse components. Thismodel is used in robust PCA and was previously suggested in the context ofultrasound Doppler processing and dynamic magnetic resonance imaging. We thenillustrate that an iterative algorithm based on this model exhibits improvedseparation of microbubble signal from the tissue signal over commonly practicedmethods. Next, we apply the concept of deep unfolding to suggest a deep networkarchitecture tailored to our clutter filtering problem which exhibits improvedconvergence speed and accuracy with respect to its iterative counterpart. Wecompare the performance of the suggested deep network on both simulations andin-vivo rat brain scans, with a commonly practiced deep-network architectureand the fast iterative shrinkage algorithm, and show that our architectureexhibits better image quality and contrast.

Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound