Sparse regularization methods in ultrafast ultrasound imaging
Sparse regularization methods in ultrafast ultrasound imaging
复制标题
超快超声成像中的稀疏正则化方法
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
J. Thiran
中科院分区：
文献类型：
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作者：
Adrien Besson;R. Carrillo;Miaomiao Zhang;D. Friboulet;O. Bernard;Y. Wiaux;J. Thiran
基于平面波失谐的超快超声成像是目前应用广泛的一种成像方式。目前提出了两种主要的图像重建方法，一种是基于经典的延迟和（DAS）方法，另一种是基于傅里叶重建方法。使用单个PW，这些方法导致图像质量低于具有多聚焦光束的DAS。本文综述了近年来基于稀疏正则化方法的波束形成方法。成像问题，无论是基于空间的（DAS）还是基于傅里叶的，都被表述为一个线性逆问题，并使用凸优化算法与稀疏先验相结合来解决不适定问题。我们描述了该框架的两种应用，即波束形成问题的稀疏反演和将该框架与压缩感知相结合的压缩波束形成。基于数值模拟和实验研究，我们证明了与经典方法相比，所提出的方法在图像质量方面的优势。
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.
影响因子：
2.9
作者：
LU, JY;GREENLEAF, JF
通讯作者：
GREENLEAF, JF