Fast and robust spatiotemporal microvessel clutter filtering with randomized singular value decomposition (rSVD) and randomized spatial downsampling

Pengfei Song, Joshua D. Trzasko, Armando Manduca, Bo Qiang, Ramanathan Kadirvel, David F. Kallmes, Shigao Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Singular value decomposition (SVD)-based clutter filtering has demonstrated superior clutter rejection performance in the emerging field of ultrasound microvessel imaging. To alleviate the computational burden of SVD, here we present a fast and robust clutter filter using randomized SVD (rSVD) and randomized spatial downsampling (rSD). rSVD accelerates SVD by approximating and removing the first k-order singular values that represent tissue, and rSD achieves further speed-up by allowing parallel processing without introducing artifacts associated with regularized downsampling.

Original languageEnglish (US)
Title of host publication2017 IEEE International Ultrasonics Symposium, IUS 2017
PublisherIEEE Computer Society
ISBN (Electronic)9781538633830
DOIs
StatePublished - Oct 31 2017
Externally publishedYes
Event2017 IEEE International Ultrasonics Symposium, IUS 2017 - Washington, United States
Duration: Sep 6 2017Sep 9 2017

Publication series

NameIEEE International Ultrasonics Symposium, IUS
ISSN (Print)1948-5719
ISSN (Electronic)1948-5727

Other

Other2017 IEEE International Ultrasonics Symposium, IUS 2017
Country/TerritoryUnited States
CityWashington
Period9/6/179/9/17

ASJC Scopus subject areas

  • Acoustics and Ultrasonics

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