Low Variance Estimation of Backscatter Quantitative Ultrasound Parameters Using Dynamic Programming.
Low Variance Estimation of Backscatter Quantitative Ultrasound Parameters Using Dynamic Programming.
复制标题
使用动态规划对反向散射定量超声参数进行低方差估计。
DOI：
10.1109/tuffc.2018.2869810
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Rivaz,Hassan
中科院分区：
文献类型：
--
作者：
Vajihi,Zara;Rosado-Mendez,IvanM;Hall,TimothyJ;Rivaz,Hassan
超声成像的主要限制之一是图像质量和解释取决于用户的技能和临床医生的经验。定量超声（QUS）方法提供对组织特性（诸如组织的声衰减和后向散射特性）的客观的、系统独立的估计，其作为诊断和干预的客观工具是有价值的。这些属性的准确和精确估计需要对介入组织衰减进行正确补偿。基于将反向散射回波数据与模型进行比较的最小化成本函数来估计介入组织衰减的先前尝试已经导致有限的精度和准确度。为了克服这些限制，在本文中，我们将QUS参数的分段连续性的先验信息作为正则化项纳入我们的成本函数。我们还建议使用动态规划（DP）来计算此成本函数，这是一种计算效率高的优化算法，可以找到全局最优值。我们的研究结果组织模仿幻影显示，DP大大优于发表的最小二乘法的估计偏差和方差。
One of the main limitations of ultrasound imaging is that image quality and interpretation depend on the skill of the user and the experience of the clinician. Quantitative ultrasound (QUS) methods provide objective, system-independent estimates of tissue properties, such as acoustic attenuation and backscattering properties of tissue, which are valuable as objective tools for both diagnosis and intervention. Accurate and precise estimation of these properties requires correct compensation for intervening tissue attenuation. Prior attempts to estimate intervening-tissue attenuation based on minimizing cost functions that compared backscattered echo data to models have resulted in limited precision and accuracy. To overcome these limitations, in this paper, we incorporate the prior information of piecewise continuity of QUS parameters as a regularization term into our cost function. We further propose to calculate this cost function using dynamic programming (DP), a computationally efficient optimization algorithm that finds the global optimum. Our results on tissue-mimicking phantoms show that DP substantially outperforms a published least squares method in terms of both estimation bias and variance.