论文标题
二阶超声弹性图，带有L1-norm空间正则化
Second-Order Ultrasound Elastography with L1-norm Spatial Regularization
论文作者
论文摘要
两个射频（RF）帧之间的时间延迟估计（TDE）是准静态超声弹性弹力的主要步骤之一，该步骤通过估计其机械性能来检测组织病理。基于正则优化的技术（一种突出的TDE算法）优化了由数据恒定和空间连续性约束组成的非线性能量功能，以获得所考虑的时间序列框架之间的位移和应变图。现有的基于优化的TDE方法通常会考虑位移衍生物的L2-NORM来构建正常化程序。然而，这种公式过度占用位移不规则性，并向估计的应变场提出了两个主要问题。首先，不同组织之间的边界是模糊的。其次，目标和背景之间的视觉对比度是次优的。为了解决这些问题，我们提出了一种新颖的TDE算法，其中考虑了一阶和二阶位移衍生物的L2-，L1-norms，以设计连续性功能。我们通过平滑绝对值函数的尖角并以迭代方式优化产生的成本函数来处理L1-norm的非差异性。我们称我们的技术为二阶超声弹性弹力，使用L1-norm空间正则化（L1-SOUL）。在清晰度和视觉对比度方面，L1-Soul在这项研究中进行的所有验证实验中最近发表的三种TDE算法都大大优于胶水，越来越多的胶水，越来越多的灵魂。在模拟，幻影和体内数据集的情况下，L1-Soul可实现67.8％，46.81％和117.35％的对比度 - 噪声比（CNR）的改善。可以从http://code.sonoghing.ai下载L1-SOUL代码。
Time delay estimation (TDE) between two radio-frequency (RF) frames is one of the major steps of quasi-static ultrasound elastography, which detects tissue pathology by estimating its mechanical properties. Regularized optimization-based techniques, a prominent class of TDE algorithms, optimize a non-linear energy functional consisting of data constancy and spatial continuity constraints to obtain the displacement and strain maps between the time-series frames under consideration. The existing optimization-based TDE methods often consider the L2-norm of displacement derivatives to construct the regularizer. However, such a formulation over-penalizes the displacement irregularity and poses two major issues to the estimated strain field. First, the boundaries between different tissues are blurred. Second, the visual contrast between the target and the background is suboptimal. To resolve these issues, herein, we propose a novel TDE algorithm where instead of L2-, L1-norms of both first- and second-order displacement derivatives are taken into account to devise the continuity functional. We handle the non-differentiability of L1-norm by smoothing the absolute value function's sharp corner and optimize the resulting cost function in an iterative manner. We call our technique Second-Order Ultrasound eLastography with L1-norm spatial regularization (L1-SOUL). In terms of both sharpness and visual contrast, L1-SOUL substantially outperforms GLUE, OVERWIND, and SOUL, three recently published TDE algorithms in all validation experiments performed in this study. In cases of simulated, phantom, and in vivo datasets, respectively, L1-SOUL achieves 67.8%, 46.81%, and 117.35% improvements of contrast-to-noise ratio (CNR) over SOUL. The L1-SOUL code can be downloaded from http://code.sonography.ai.
