论文标题
超声波横梁的逆问题，具有基于denoising的正则解决方案
Inverse Problem of Ultrasound Beamforming with Denoising-Based Regularized Solutions
论文作者
论文摘要
在过去的几年中，超声梁形成的反问题制剂引起了人们日益增长的兴趣。它们通常是由测量模型以及正则化项产生的忠诚项的最小化问题，并在产生图像上执行某个类别。本文中，我们采用乘数交替方向方法的优点，提出了一个灵活的框架，其中每个项都会分别优化。此外，根据最近称为插入和播放的方法（PNP）的方法，扩展了提出的波束形成公式以替换正规化项，并通过denoing（红色）正规化。在这项工作中显示了这样的正常化，以更好地保留斑点纹理，这是超声成像中的重要特征，而不是先前在文献中提出的基于稀疏性的方法。在Medical Ultrasound中的平面波成像挑战中获得的模拟，真实幻像{in Vivo}数据评估了提出的方法的效率。此外，还提供了与现有的超声波束形成方法的全面比较。这些结果表明，红色算法在保留斑点统计数据的同时，就对比度指数提供了最佳的图像质量。
During the past few years, inverse problem formulations of ultrasound beamforming have attracted a growing interest. They usually pose beamforming as a minimization problem of a fidelity term resulting from the measurement model plus a regularization term that enforces a certain class on the resulting image. Herein, we take advantages of alternating direction method of multipliers to propose a flexible framework in which each term is optimized separately. Furthermore, the proposed beamforming formulation is extended to replace the regularization term by a denoising algorithm, based on the recent approaches called plug-and-play (PnP) and regularization by denoising (RED). Such regularizations are shown in this work to better preserve speckle texture, an important feature in ultrasound imaging, than sparsity-based approaches previously proposed in the literature. The efficiency of proposed methods is evaluated on simulations, real phantoms, and \textit{in vivo} data available from a plane-wave imaging challenge in medical ultrasound. Furthermore, a comprehensive comparison with existing ultrasound beamforming methods is also provided. These results show that the RED algorithm gives the best image quality in terms of contrast index while preserving the speckle statistics.
