000218362 001__ 218362
000218362 005__ 20190317000441.0
000218362 020__ $$a978-0-9928-6265-7
000218362 022__ $$a2076-1465
000218362 02470 $$2ISI$$a000391891900108
000218362 037__ $$aCONF
000218362 245__ $$aSparse regularization methods in ultrafast ultrasound imaging
000218362 269__ $$a2016
000218362 260__ $$bIeee$$c2016$$aNew York
000218362 300__ $$a5
000218362 336__ $$aConference Papers
000218362 490__ $$aEuropean Signal Processing Conference
000218362 520__ $$aUltrafast ultrasound (US) imaging based on plane wave (PW) insonification is a widely used modality nowadays. Two main types of approaches have been proposed for image reconstruction either based on classical delay-and-sum (DAS) or on Fourier reconstruction. Using a single PW, these methods lead to a lower image quality than DAS with multi-focused beams. In this paper we review recent beamforming approaches based on sparse regularization methods. The imaging problem, either spatial-based (DAS) or Fourier-based, is formulated as a linear inverse problem and convex optimization algorithms coupled with sparsity priors are used to solve the ill-posed problem. We describe two applications of the framework namely the sparse inversion of the beamforming problem and the compressed beamforming in which the framework is combined with compressed sensing. Based on numerical simulations and experimental studies, we show the advantage of the proposed methods in terms of image quality compared to classical methods.
000218362 6531_ $$aUltrasound
000218362 6531_ $$aplane wave imaging
000218362 6531_ $$asparsity
000218362 6531_ $$acompressed sensing
000218362 700__ $$0248789$$g214640$$aBesson, Adrien Georges Jean
000218362 700__ $$0245580$$g215293$$aCarrillo, Rafael
000218362 700__ $$aZhang, Miaomiao
000218362 700__ $$aFriboulet, Denis
000218362 700__ $$aBernard, Olivier
000218362 700__ $$0240427$$g163268$$aWiaux, Yves
000218362 700__ $$0240323$$g115534$$aThiran, Jean-Philippe
000218362 7112_ $$dSeptember, 2016$$cBudapest, Hungary$$a2016 European Signal Processing Conference (EUSIPCO)
000218362 773__ $$t2016 24Th European Signal Processing Conference (Eusipco)$$q552-556
000218362 8564_ $$uhttps://infoscience.epfl.ch/record/218362/files/EUSIPCO2016_Besson.pdf$$zSlides of the presentation at EUSIPCO 2016$$s1408658$$ySlides of the presentation at EUSIPCO 2016
000218362 8564_ $$uhttps://infoscience.epfl.ch/record/218362/files/eusipco_2016_Besson.pdf$$zn/a$$s816946$$yn/a
000218362 909C0 $$xU10954$$0252394$$pLTS5
000218362 909CO $$ooai:infoscience.tind.io:218362$$qGLOBAL_SET$$pconf$$pSTI
000218362 917Z8 $$x214640
000218362 917Z8 $$x215293
000218362 917Z8 $$x214640
000218362 917Z8 $$x214640
000218362 917Z8 $$x214640
000218362 937__ $$aEPFL-CONF-218362
000218362 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000218362 980__ $$aCONF