Compressed sensing or compressive sampling is a recent theory that originated in the applied mathematics field. It suggests a robust\r\nway to sample signals or images below the classic Shannon-Nyquist theorem limit. This technique has led to many applications,\r\nand has especially been successfully used in diverse medical imaging modalities such as magnetic resonance imaging, computed\r\ntomography, or photoacoustics. This paper first revisits the compressive sampling theory and then proposes several strategies to\r\nperform compressive sampling in the context of ultrasound imaging. Finally, we show encouraging results in 2D and 3D, on highand\r\nlow-frequency ultrasound images.
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