Blind Source Separation for Clutter and Noise Suppression in Ultrasound Imaging: Review for Different Applications
| Autor: | Chiara Rabotti, F. Sammali, Sergei Shulepov, Georg Salomon, Benedictus C. Schoot, R.J.G. van Sloun, Hessel Wijkstra, Y. Huang, Peiran Chen, R.R. Wildeboer, Massimo Mischi, Matthew Bruce |
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| Přispěvatelé: | Center for Care & Cure Technology Eindhoven, Biomedical Diagnostics Lab, Signal Processing Systems, EAISI Health |
| Rok vydání: | 2020 |
| Předmět: |
Acoustics and Ultrasonics
Computer science super-resolution 01 natural sciences Signal Blind signal separation Blind source separation (BSS) microbubbles ultrasound imaging Speckle pattern 0103 physical sciences principal component analysis (PCA) Electrical and Electronic Engineering 010301 acoustics Instrumentation Signal processing Noise (signal processing) business.industry Pattern recognition nonnegative matrix factorization (NMF) Independent component analysis speckle tracking (ST) Filter design Clutter singular value decomposition (SVD) sense organs Artificial intelligence independent component analysis (ICA) business contrast-enhanced ultrasound |
| Zdroj: | IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(8):9005396, 1497-1512. Institute of Electrical and Electronics Engineers |
| ISSN: | 1525-8955 0885-3010 |
| DOI: | 10.1109/tuffc.2020.2975483 |
| Popis: | 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 the 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 super-resolution, 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 highlighted the value of a priori domain knowledge to define effective criteria for signal component selection. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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