2012
DOI: 10.1109/tsp.2012.2200891
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Compressed Beamforming in Ultrasound Imaging

Abstract: Abstract-Emerging sonography techniques often require increasing the number of transducer elements involved in the imaging process. Consequently, larger amounts of data must be acquired and processed. The significant growth in the amounts of data affects both machinery size and power consumption. Within the classical sampling framework, state of the art systems reduce processing rates by exploiting the bandpass bandwidth of the detected signals. It has been recently shown, that a much more significant sample-r… Show more

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Cited by 188 publications
(201 citation statements)
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“…as defined in (6). In practice one usually has no knowledge about the structure of w and thus γ needs to be chosen arbitrarily.…”
Section: Remark 4 This Proposition Characterizes a Tradeoff Betweenmentioning
confidence: 99%
See 1 more Smart Citation
“…as defined in (6). In practice one usually has no knowledge about the structure of w and thus γ needs to be chosen arbitrarily.…”
Section: Remark 4 This Proposition Characterizes a Tradeoff Betweenmentioning
confidence: 99%
“…. , λ} with λ < k and γ opt λ as in (6). Equations (13)- (14) characterize the requirement for complete support recovery, whereas (15)- (16) Proof: The proof is provided in Appendix D-A.…”
Section: Region Of Interestmentioning
confidence: 99%
“…In recent years, the area of CS has branched out to a number of new fronts and has worked its way into several application areas, such as radar, communications, and ultrasound imaging. Eldar and her colleagues published extensively in applying CS theory to ultrasound imaging [4][5][6][7]. The key idea in all of these works is that the ultrasound signal can be modeled as Finite Rate of Innovation (FRI) signals which is a sum of a weighted and delayed known signal [8].…”
Section: Introductionmentioning
confidence: 99%
“…This has received much attention [1][2][3][4] and has several applications in signal processing [5][6][7][8][9]. One convex method for finding sparse representations is basis-pursuit [10] (or 1-minimization) which finds a sparse solution asx…”
Section: Introductionmentioning
confidence: 99%