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Clutter Suppression in Ultrasound: Performance Evaluation of Low-Rank and Sparse Matrix Decomposition Methods


Clutter Suppression in Ultrasound: Performance Evaluation of Low-Rank and Sparse Matrix Decomposition Methods

Zhang, Naiyuan ORCID: https://orcid.org/0000-0001-7620-8842 (2020) Clutter Suppression in Ultrasound: Performance Evaluation of Low-Rank and Sparse Matrix Decomposition Methods. Masters thesis, Concordia University.

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Vessel diseases are often accompanied by abnormalities related to vascular shape and size. Therefore, a clear visualization of vasculature is of high clinical significance. Ultrasound Color Flow Imaging (CFI) is one of the prominent techniques for flow visualization. However, clutter signals originating from slow-moving tissue is one of the main obstacles to obtain a clear view of the vascular network. Enhancement of the vasculature by suppressing the clutters is an essential step for many applications of ultrasound CFI. In this thesis, we focus on a state-of-art algorithm framework called Decomposition into Low-rank and Sparse Matrices (DLSM) framework for ultrasound clutter suppression.
Currently, ultrasound clutter suppression is often performed by Singular Value Decomposition (SVD) of the data matrix, which is a branch of eigen-based filtering. This approach exhibits two well-known limitations. First, the performance of SVD is sensitive to the proper manual selection of the ranks corresponding to clutter and blood subspaces. Second, SVD is prone to failure in the presence of large random noise in the data set. A potential solution to these issues is the use of DLSM framework. SVD, as a means for singular values, is also one of the widely used algorithms for solving the minimization problem under the DLSM framework. Many other algorithms under DLSM avoid full SVD and use approximated SVD or SVD-free ideas which may have better performance with higher robustness and lower computing time due to the expensive computational cost of full SVD. In practice, these models separate blood from clutter based on the assumption that steady clutter represents a low-rank structure and the moving blood component is sparse.
In this thesis, we exploit the feasibility of exploiting low-rank and sparse decomposition schemes, originally developed in the field of computer vision, in ultrasound clutter suppression. Since ultrasound images have different texture and statistical properties compared to images in computer vision, it is of high importance to evaluate how these methods translate to ultrasound CFI. We conduct this evaluation study by adapting 106 DLSM algorithms and validating them against simulation, phantom and in vivo rat data sets.
The advantage of simulation and phantom experiments is that the ground truth vessel map is known, and the advantage of the in vivo data set is that it enables us to test algorithms in a realistic setting. Two conventional quality metrics, Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), are used for performance evaluation. In addition, computation times required by different algorithms for generating the clutter suppressed images are reported. Our extensive analysis shows that the DLSM framework can be successfully applied to ultrasound clutter suppression.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Zhang, Naiyuan
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:5 May 2020
Thesis Supervisor(s):Rivaz, Hassan
Keywords:Ultrasound color flow imaging; Clutter suppression; Vessel visualization; Low-rank and sparse matrix decomposition
ID Code:986837
Deposited By: Naiyuan Zhang
Deposited On:27 Oct 2022 13:50
Last Modified:27 Oct 2022 13:50
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