论文标题
使用ADMM的超声应变成像
Ultrasound Strain Imaging using ADMM
论文作者
论文摘要
超声应变成像描述了机械性能以检测组织异常，涉及估计在组织变形前和之后收集的两个射频（RF）帧之间的时间延迟。现有的基于正规化优化的时间延迟估计（TDE）技术至少遭受以下缺点之一：1）由于仅考虑了一阶位移衍生物，因此正常器与组织变形物理学不符。 2）位移衍生物的L2-norm被用作正规器。 3）绝对值函数应通过平滑函数近似，以促进L1-norm的优化。在本文中，为了解决这些缺点，我们建议采用乘数的交替方向方法（ADMM）优化由L2-norm数据保真度项和L1-norm的一阶和二阶空间连续性项组成的新型成本函数。 ADMM赋予所提出的算法使用不同的技术来优化成本函数的不同部分，并获得具有光滑背景和锋利边界的高对比度应变图像。我们将技术ADMM命名为超声菌株成像中的总变异正则化（利他主义者）。在广泛的模拟，幻影和体内实验中，利他主义者在定性和定量上都超过了三种最近出版的TDE算法的胶水，越来越多的TDE算法。对于模拟，幻影和体内肝癌数据集，利他主义者比L1-SOUL的对比度比率分别提高了118％，104％和72％。在接受本文后，我们将在http://code.sonography.ai接受本文后发布“利他主义代码”。
Ultrasound strain imaging, which delineates mechanical properties to detect tissue abnormalities, involves estimating the time-delay between two radio-frequency (RF) frames collected before and after tissue deformation. The existing regularized optimization-based time-delay estimation (TDE) techniques suffer from at least one of the following drawbacks: 1) The regularizer is not aligned with tissue deformation physics due to taking only the first-order displacement derivative into account. 2) The L2-norm of the displacement derivatives, which oversmooths the estimated time-delay, is utilized as the regularizer. 3) The absolute value function should be approximated by a smooth function to facilitate the optimization of L1-norm. Herein, to resolve these shortcomings, we propose employing the alternating direction method of multipliers (ADMM) for optimizing a novel cost function consisting of L2-norm data fidelity term and L1-norm first- and second-order spatial continuity terms. ADMM empowers the proposed algorithm to use different techniques for optimizing different parts of the cost function and obtain high-contrast strain images with smooth background and sharp boundaries. We name our technique ADMM for totaL variaTion RegUlarIzation in ultrasound STrain imaging (ALTRUIST). In extensive simulation, phantom, and in vivo experiments, ALTRUIST substantially outperforms GLUE, OVERWIND, and L1-SOUL, three recently-published TDE algorithms, both qualitatively and quantitatively. ALTRUIST yields 118%, 104%, and 72% improvements of contrast-to-noise ratio over L1-SOUL for simulated, phantom, and in vivo liver cancer datasets, respectively. We will publish the ALTRUIST code after the acceptance of this paper at http://code.sonography.ai.
