Deep Unfolded Robust PCA With Application to Clutter Suppression in Ultrasound

• Published in: IEEE transactions on medical imaging Vol. 39; no. 4; pp. 1051 - 1063
• Main Authors: Solomon, Oren, Cohen, Regev, Zhang, Yi, Yang, Yi, He, Qiong, Luo, Jianwen, van Sloun, Ruud J. G., Eldar, Yonina C.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Animals; Blood; Brain - blood supply; Brain - diagnostic imaging; Clutter; Computer simulation; Contrast Media; Deep unfolding; Doppler effect; Image contrast; Image Processing, Computer-Assisted - methods; Image quality; Imaging; inverse problems; Iterative algorithms; Iterative methods; Machine learning; Magnetic resonance imaging; Microbubbles; neural network; Neuroimaging; Principal component analysis; Principal Component Analysis - methods; Principal components analysis; Radiation; Rats; robust PCA; Robustness; Sparse matrices; Spatial discrimination; Spatial resolution; Tissues; Ultrasonic imaging; Ultrasonography - methods; Ultrasound; ultrasound imaging
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2019.2941271

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.