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A Sparse Reconstruction Framework for Fourier-Based Plane-Wave Imaging.

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Bibliographic Details
Title:	A Sparse Reconstruction Framework for Fourier-Based Plane-Wave Imaging.
Authors:	Besson, Adrien¹, Zhang, Miaomiao², Varray, Francois², Liebgott, Herve², Friboulet, Denis², Wiaux, Yves³, Thiran, Jean-Philippe¹, Carrillo, Rafael E.¹, Bernard, Olivier²
Source:	IEEE Transactions on Ultrasonics Ferroelectrics & Frequency Control. Dec2016, Vol. 63 Issue 12, p2092-2106. 15p.
Subjects:	Plane wavefronts, Compressed sensing, Magnetic resonance imaging, Image reconstruction, Image quality analysis
Abstract:	Ultrafast imaging based on plane-wave (PW) insonification is an active area of research due to its capability of reaching high frame rates. Among PW imaging methods, Fourier-based approaches have demonstrated to be competitive compared with traditional delay and sum methods. Motivated by the success of compressed sensing techniques in other Fourier imaging modalities, like magnetic resonance imaging, we propose a new sparse regularization framework to reconstruct highquality ultrasound (US) images. The framework takes advantage of both the ability to formulate the imaging inverse problem in the Fourier domain and the sparsity of US images in a sparsifying domain. We show, by means of simulations, in vitro and in vivo data, that the proposed framework significantly reduces image artifacts, i.e., measurement noise and sidelobes, compared with classical methods, leading to an increase of the image quality. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Ultrafast imaging based on plane-wave (PW) insonification is an active area of research due to its capability of reaching high frame rates. Among PW imaging methods, Fourier-based approaches have demonstrated to be competitive compared with traditional delay and sum methods. Motivated by the success of compressed sensing techniques in other Fourier imaging modalities, like magnetic resonance imaging, we propose a new sparse regularization framework to reconstruct highquality ultrasound (US) images. The framework takes advantage of both the ability to formulate the imaging inverse problem in the Fourier domain and the sparsity of US images in a sparsifying domain. We show, by means of simulations, in vitro and in vivo data, that the proposed framework significantly reduces image artifacts, i.e., measurement noise and sidelobes, compared with classical methods, leading to an increase of the image quality. [ABSTRACT FROM AUTHOR]
ISSN:	08853010
DOI:	10.1109/TUFFC.2016.2614996