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Article: Frequency-based signal processing for ultrasound color flow imaging

TitleFrequency-based signal processing for ultrasound color flow imaging
Authors
Issue Date2007
Citation
Canadian Acoustics - Acoustique Canadienne, 2007, v. 35 n. 2, p. 11-23 How to Cite?
AbstractIn ultrasound color flow imaging, the computation of flow estimates is well-recognized as a challenging problem from a signal processing perspective. The flow visualization performance of this imaging tool is often affected by error sources such as the lack of abundant signal samples available for processing, the presence of wideband clutter in the acquired signals, and the flow signal distortions that may arise during clutter suppression. In this article, we review existing frequency-based signal processing approaches reported in the ultrasound literature and evaluate their theoretical advantages as well as limitations. In particular, four major classes of clutter filter designs are considered: FIR/IIR filtering, polynomial regression, clutter-downmixing, and eigen-regression. Also, three types of frequency estimators are discussed: lag-one autocorrelation, autoregressive modeling, and MUSIC. In examining these approaches, it was concluded that eigen-based methods like the eigen-regression filter and the MUSIC estimator can better adapt to the Doppler signal characteristics, and thus they seem to have more potential for obtaining flow estimates that are less affected by the signal processing error sources.
Persistent Identifierhttp://hdl.handle.net/10722/155384
ISSN
2015 SCImago Journal Rankings: 0.106
References

 

DC FieldValueLanguage
dc.contributor.authorYu, ACHen_US
dc.contributor.authorJohnston, KWen_US
dc.contributor.authorCobbold, RSCen_US
dc.date.accessioned2012-08-08T08:33:13Z-
dc.date.available2012-08-08T08:33:13Z-
dc.date.issued2007en_US
dc.identifier.citationCanadian Acoustics - Acoustique Canadienne, 2007, v. 35 n. 2, p. 11-23en_US
dc.identifier.issn0711-6659en_US
dc.identifier.urihttp://hdl.handle.net/10722/155384-
dc.description.abstractIn ultrasound color flow imaging, the computation of flow estimates is well-recognized as a challenging problem from a signal processing perspective. The flow visualization performance of this imaging tool is often affected by error sources such as the lack of abundant signal samples available for processing, the presence of wideband clutter in the acquired signals, and the flow signal distortions that may arise during clutter suppression. In this article, we review existing frequency-based signal processing approaches reported in the ultrasound literature and evaluate their theoretical advantages as well as limitations. In particular, four major classes of clutter filter designs are considered: FIR/IIR filtering, polynomial regression, clutter-downmixing, and eigen-regression. Also, three types of frequency estimators are discussed: lag-one autocorrelation, autoregressive modeling, and MUSIC. In examining these approaches, it was concluded that eigen-based methods like the eigen-regression filter and the MUSIC estimator can better adapt to the Doppler signal characteristics, and thus they seem to have more potential for obtaining flow estimates that are less affected by the signal processing error sources.en_US
dc.languageengen_US
dc.relation.ispartofCanadian Acoustics - Acoustique Canadienneen_US
dc.titleFrequency-based signal processing for ultrasound color flow imagingen_US
dc.typeArticleen_US
dc.identifier.emailYu, ACH:alfred.yu@hku.hken_US
dc.identifier.authorityYu, ACH=rp00657en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-34547353592en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34547353592&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume35en_US
dc.identifier.issue2en_US
dc.identifier.spage11en_US
dc.identifier.epage23en_US
dc.publisher.placeCanadaen_US
dc.identifier.scopusauthoridYu, ACH=8699317700en_US
dc.identifier.scopusauthoridJohnston, KW=7202814635en_US
dc.identifier.scopusauthoridCobbold, RSC=7005052711en_US

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