Chaturvedi P, Insana M F
Department of Radiology, University of Kansas Medical Center, Kansas City 66160-7234, USA.
IEEE Trans Med Imaging. 1998 Feb;17(1):53-61. doi: 10.1109/42.668694.
Maximum likelihood (ML) methods are widely used in acoustic parameter estimation. Although ML methods are often unbiased, the variance is unacceptably large for many applications, including medical imaging. For such cases, Bayesian estimators can reduce variance and preserve contrast at the cost of an increased bias. Consequently, including prior knowledge about object and noise properties in the estimator can improve low-contrast target detectability of parametric ultrasound images by improving the precision of the estimates. In this paper, errors introduced by biased estimators are analyzed and approximate closed-form expressions are developed. The task-specific nature of the estimator performance is demonstrated through analysis, simulation, and experimentation. A strategy for selecting object priors is proposed. Acoustic scattering from kidney tissue is the emphasis of this paper, although the results are more generally applicable.
最大似然（ML）方法在声学参数估计中被广泛使用。尽管ML方法通常是无偏的，但对于包括医学成像在内的许多应用而言，其方差大得令人无法接受。对于此类情况，贝叶斯估计器可以降低方差并保持对比度，但代价是偏差增加。因此，在估计器中纳入关于对象和噪声特性的先验知识，可以通过提高估计精度来改善参数超声图像的低对比度目标可检测性。本文分析了有偏估计器引入的误差，并推导了近似的闭式表达式。通过分析、模拟和实验证明了估计器性能的任务特定性质。提出了一种选择对象先验的策略。本文重点关注肾脏组织的声学散射，不过结果更具普遍适用性。
