A sparse regularization approach for ultrafast ultrasound imaging

Olivier Bernard, Adrien Georges Jean Besson, Rafael Eduardo Carrillo Rangel, Jean-Philippe Thiran, Yves Wiaux
IEEE, 2015
Conference paper

Abstract

Ultrafast imaging based on plane-wave (PW) in- sonification is an active area of research due to its capability of reaching high frame rates. Several approaches have been proposed either based on either of Fourier-domain reconstruction or on delay-and-sum (DAS) reconstruction. Using a single PW, these techniques achieve low quality, in terms of resolution and contrast, compared to the classic DAS method with focused beams. To overcome this drawback, compounding of several steered PWs is needed, which currently decreases the high frame rate limit that could be reached by such techniques. Based on a compressed sensing (CS) framework, we propose a new method that allows the reconstruction of high quality ultrasound (US) images from only 1 PW at the expense of augmenting the computational complexity at the reconstruction.

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https://infoscience.epfl.ch/record/211217?ln=en

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Olivier Bernard, Adrien Georges Jean Besson, Rafael Eduardo Carrillo Rangel, Jean-Philippe Thiran, Yves Wiaux
IEEE, 2015
Conference paper

Abstract

Official source

https://infoscience.epfl.ch/record/211217?ln=en

About this result

Related concepts (12)

Ultrasound is sound with frequencies greater than 20 kilohertz. This frequency is the approximate upper audible limit of human hearing in healthy young adults. The physical principles of acoustic wa

In physics, a plane wave is a special case of wave or field: a physical quantity whose value, at any moment, is constant through any plane that is perpendicular to a fixed direction in space. For

In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured

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Sparse regularization methods in ultrafast ultrasound imaging

Olivier Bernard, Adrien Georges Jean Besson, Rafael Eduardo Carrillo Rangel, Jean-Philippe Thiran, Yves Wiaux

Ultrafast 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.

Ieee2016

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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 to 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 high quality ultrasound images. The framework takes advantage of both the ability to formulate the imaging inverse problem in the Fourier domain and the sparsity of ultrasound 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 side lobes, compared to classical methods, leading to an increase of the image quality.

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