Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.
Comput Biol Med. 2024 Feb;169:107860. doi: 10.1016/j.compbiomed.2023.107860. Epub 2023 Dec 23.
The application of ultrasound (US) image has been limited by its limited resolution, inherent speckle noise, and the impact of clutter and artifacts, especially in the miniaturized devices with restricted hardware conditions. In order to solve these problems, many researchers have explored a number of hardware modifications as well as algorithmic improvements, but further improvements in resolution, signal-to-noise ratio (SNR) and contrast are still needed. In this paper, a deconvolution algorithm based on sparsity and continuity (DBSC) is proposed to obtain the higher resolution, SNR, and, contrast. The algorithm begins with a relatively bold Wiener filtering for initial enhancement of image resolution in preprocessing, but it also introduces ringing noise and compromises the SNR. In further processing, the noise is suppressed based on the characteristic that the adjacent pixels of the US image are continuous as long as Nyquist sampling criterion is met, and the extraction of high-frequency information is balanced by using relatively sparse. Subsequently, the theory and experiments demonstrate that relative sparsity and continuity are general properties of US images. DBSC is compared with other deconvolution strategies through simulations and experiments, and US imaging under different transmission channels is also investigated. The final results show that the proposed method can greatly improve the resolution, as well as provide significant advantages in terms of contrast and SNR, and is also feasible in applications to devices with limited hardware.
超声（US）图像的应用受到其有限的分辨率、固有斑点噪声以及杂波和伪影的影响的限制，尤其是在硬件条件受限的小型化设备中。为了解决这些问题，许多研究人员探索了许多硬件改进和算法改进，但分辨率、信噪比（SNR）和对比度的进一步提高仍然是需要的。在本文中，提出了一种基于稀疏性和连续性的反卷积算法（DBSC），以获得更高的分辨率、SNR 和对比度。该算法从预处理中相对大胆的维纳滤波开始，以初始增强图像分辨率，但它也引入了振铃噪声并影响了 SNR。在进一步处理中，只要满足奈奎斯特采样准则，就可以利用 US 图像的相邻像素是连续的特性来抑制噪声，并通过相对稀疏来平衡高频信息的提取。随后，理论和实验证明相对稀疏性和连续性是 US 图像的一般特性。通过模拟和实验将 DBSC 与其他反卷积策略进行了比较，还研究了不同传输通道下的 US 成像。最终结果表明，该方法可以大大提高分辨率，并在对比度和 SNR 方面提供显著优势，在硬件受限的设备中也具有可行性。
