A Sparse Reconstruction Framework for Fourier-Based Plane-Wave Imaging
A Sparse Reconstruction Framework for Fourier-Based Plane-Wave Imaging
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DOI：
10.1109/tuffc.2016.2614996
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Adrien Besson;Miaomiao Zhang;F. Varray;H. Liebgott;D. Friboulet;Y. Wiaux;J. Thiran;R. Carrillo;O. Bernard
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文献类型：
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作者：
Adrien Besson;Miaomiao Zhang;F. Varray;H. Liebgott;D. Friboulet;Y. Wiaux;J. Thiran;R. Carrillo;O. Bernard
基于平面波（PW）声穿透的超快成像由于其达到高帧速率的能力而成为研究的活跃领域。在PW成像方法中，与传统的延迟和求和方法相比，基于傅立叶的方法已被证明具有竞争力。受压缩感知技术在其他傅立叶成像模式（如磁共振成像）中的成功的启发，我们提出了一种新的稀疏正则化框架来重建高质量的超声（US）图像。该框架利用了在傅立叶域中制定成像逆问题的能力和在稀疏化域中US图像的稀疏性。我们通过模拟，在体外和体内的数据，所提出的框架显着减少图像伪影，即，测量噪声和旁瓣，与经典的方法相比，导致图像质量的增加。
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 high-quality 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.