Chen Zhouye, Basarab Adrian, Kouamé Denis
IEEE Trans Med Imaging. 2016 Mar;35(3):728-37. doi: 10.1109/TMI.2015.2493241. Epub 2015 Oct 26.
The interest of compressive sampling in ultrasound imaging has been recently extensively evaluated by several research teams. Following the different application setups, it has been shown that the RF data may be reconstructed from a small number of measurements and/or using a reduced number of ultrasound pulse emissions. Nevertheless, RF image spatial resolution, contrast and signal to noise ratio are affected by the limited bandwidth of the imaging transducer and the physical phenomenon related to US wave propagation. To overcome these limitations, several deconvolution-based image processing techniques have been proposed to enhance the ultrasound images. In this paper, we propose a novel framework, named compressive deconvolution, that reconstructs enhanced RF images from compressed measurements. Exploiting an unified formulation of the direct acquisition model, combining random projections and 2D convolution with a spatially invariant point spread function, the benefit of our approach is the joint data volume reduction and image quality improvement. The proposed optimization method, based on the Alternating Direction Method of Multipliers, is evaluated on both simulated and in vivo data.
最近，几个研究团队对压缩采样在超声成像中的应用进行了广泛评估。根据不同的应用设置，研究表明，可以从少量测量值和/或使用减少数量的超声脉冲发射来重建射频数据。然而，射频图像的空间分辨率、对比度和信噪比会受到成像换能器有限带宽以及与超声波传播相关的物理现象的影响。为了克服这些限制，人们提出了几种基于反卷积的图像处理技术来增强超声图像。在本文中，我们提出了一种名为压缩反卷积的新颖框架，该框架可从压缩测量中重建增强的射频图像。利用直接采集模型的统一公式，将随机投影和二维卷积与空间不变点扩散函数相结合，我们方法的优点是在减少数据量的同时提高图像质量。基于交替方向乘子法提出的优化方法在模拟数据和体内数据上均进行了评估。
