Blind source separation for clutter and noise suppression in ultrasound imaging: review for different applications

R.R. Wildeboer (Corresponding author), F. Sammali (Corresponding author), R.J.G. van Sloun (Corresponding author), Y. Huang, Peiran Chen, M. Bruce, C. Rabotti, S. Shulepov, G. Salomon, B.C. Schoot, H. Wijkstra, M. Mischi

Research output: Contribution to journalArticleAcademicpeer-review

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Blind Source Separation (BSS) refers to a number of signal processing techniques that decompose a signal into several "source" signals. In recent years, BSS is increasingly employed for the suppression of clutter and noise in ultrasonic imaging. In particular, its ability to separate sources based on measures of independence rather than their temporal or spatial frequency content, makes BSS a powerful filtering tool for data in which the desired and undesired signals overlap in the spectral domain. The purpose of this work was to review existing BSS methods and their potential in ultrasound imaging. Furthermore, we tested and compared the effectiveness of these techniques in the field of contrast-ultrasound superresolution, contrast quantification, and speckle tracking. For all applications, this was done in silico, in vitro, and in vivo. We found that the critical step in BSS filtering is the identification of components containing the desired signal and we highlighted the value of a priori domain knowledge to define effective criteria for signal component selection.

Original languageEnglish
Article number9005396
Pages (from-to)1497-1512
Number of pages16
JournalIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Issue number8
Early online date20 Feb 2020
Publication statusPublished - 1 Aug 2020


FundersFunder number
KWF Dutch Cancer SocietyUVA2013-5941
Eindhoven University of Technology
SuperSonic Imagine
Seventh Framework Programme280209
European Research CouncilHTSM-13901


    • Blind source separation (BSS)
    • contrast-enhanced ultrasound
    • independent component analysis (ICA)
    • microbubbles
    • nonnegative matrix factorization (NMF)
    • principal component analysis (PCA)
    • speckle tracking (ST)
    • super-resolution
    • singular value decomposition (SVD)
    • ultrasound imaging


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