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

机译:基于傅里叶平面波成像的稀疏重建框架

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摘要

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.
机译:基于平面波(PW)声化的超快成像由于具有达到高帧频的能力而成为研究的活跃领域。在PW成像方法中,基于傅立叶的方法已证明与传统的延迟和求和方法相比具有竞争力。受压缩感测技术在其他傅里叶成像模式(例如磁共振成像)中获得成功的推动,我们提出了一种新的稀疏正则化框架来重构高质量超声(US)图像。该框架利用了在傅立叶域中表达成像逆问题的能力和在稀疏域中美国图像的稀疏性。通过模拟,我们显示了体外和体内数据,与经典方法相比,提出的框架显着减少了图像伪像,即测量噪声和旁瓣,从而提高了图像质量。

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