TY - JOUR
T1 - Blind source separation for clutter and noise suppression in ultrasound imaging
T2 - review for different applications
AU - Wildeboer, R.R.
AU - Sammali, F.
AU - van Sloun, R.J.G.
AU - Huang, Y.
AU - Chen, Peiran
AU - Bruce, M.
AU - Rabotti, C.
AU - Shulepov, S.
AU - Salomon, G.
AU - Schoot, B.C.
AU - Wijkstra, H.
AU - Mischi, M.
PY - 2020/8/1
Y1 - 2020/8/1
N2 - 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.
AB - 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.
KW - Blind source separation (BSS)
KW - contrast-enhanced ultrasound
KW - independent component analysis (ICA)
KW - microbubbles
KW - nonnegative matrix factorization (NMF)
KW - principal component analysis (PCA)
KW - speckle tracking (ST)
KW - super-resolution
KW - singular value decomposition (SVD)
KW - ultrasound imaging
UR - http://www.scopus.com/inward/record.url?scp=85088678163&partnerID=8YFLogxK
U2 - 10.1109/TUFFC.2020.2975483
DO - 10.1109/TUFFC.2020.2975483
M3 - Article
C2 - 32091998
SN - 0885-3010
VL - 67
SP - 1497
EP - 1512
JO - IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
JF - IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
IS - 8
M1 - 9005396
ER -