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Frequency Domain Compressive Sampling for Ultrasound Imaging
Author(s) -
Céline Quinsac,
Adrian Basarab,
Denis Kouamé
Publication year - 2012
Publication title -
advances in acoustics and vibration
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.237
H-Index - 14
eISSN - 1687-627X
pISSN - 1687-6261
DOI - 10.1155/2012/231317
Subject(s) - compressed sensing , nyquist–shannon sampling theorem , sampling (signal processing) , context (archaeology) , computer science , frequency domain , magnetic resonance imaging , sample (material) , acoustics , computer vision , algorithm , radiology , physics , medicine , geology , paleontology , filter (signal processing) , thermodynamics
Compressed sensing or compressive sampling is a recent theory that originated in the applied mathematics field. It suggests a robust way to sample signals or images below theclassic Shannon-Nyquist theorem limit. This technique has led to many applications, and has especially been successfully used in diverse medical imaging modalities such as magnetic resonance imaging, computed tomography, or photoacoustics. This paper first revisits the compressive sampling theory and then proposes several strategies to perform compressive sampling in the context of ultrasound imaging. Finally, we show encouraging results in 2D and 3D, on high- and low-frequency ultrasound images

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