Dumont Douglas M, Byram Brett C
IEEE Trans Ultrason Ferroelectr Freq Control. 2016 Jan;63(1):20-34. doi: 10.1109/TUFFC.2015.2495111. Epub 2015 Oct 27.
Radiation-force-based elasticity imaging describes a group of techniques that use acoustic radiation force (ARF) to displace tissue to obtain qualitative or quantitative measurements of tissue properties. Because ARF-induced displacements are on the order of micrometers, tracking these displacements in vivo can be challenging. Previously, it has been shown that Bayesian-based estimation can overcome some of the limitations of a traditional displacement estimator such as normalized cross-correlation (NCC). In this work, we describe a Bayesian framework that combines a generalized Gaussian-Markov random field (GGMRF) prior with an automated method for selecting the prior's width. We then evaluate its performance in the context of tracking the micrometer-order displacements encountered in an ARF-based method such as ARF impulse (ARFI) imaging. The results show that bias, variance, and mean-square error (MSE) performance vary with prior shape and width, and that an almost one order-of-magnitude reduction in MSE can be achieved by the estimator at the automatically selected prior width. Lesion simulations show that the proposed estimator has a higher contrast-to-noise ratio but lower contrast than NCC, median-filtered NCC, and the previous Bayesian estimator, with a non-Gaussian prior shape having better lesion-edge resolution than a Gaussian prior. In vivo results from a cardiac, radio-frequency ablation ARFI imaging dataset show quantitative improvements in lesion contrast-to-noise ratio over NCC as well as the previous Bayesian estimator.
基于辐射力的弹性成像描述了一组使用声辐射力（ARF）使组织位移以获得组织特性定性或定量测量的技术。由于ARF引起的位移在微米量级，在体内跟踪这些位移具有挑战性。此前已表明，基于贝叶斯的估计可以克服传统位移估计器（如归一化互相关（NCC））的一些局限性。在这项工作中，我们描述了一个贝叶斯框架，该框架将广义高斯 - 马尔可夫随机场（GGMRF）先验与一种用于选择先验宽度的自动方法相结合。然后，我们在跟踪基于ARF的方法（如ARF脉冲（ARFI）成像）中遇到的微米级位移的背景下评估其性能。结果表明，偏差、方差和均方误差（MSE）性能随先验形状和宽度而变化，并且在自动选择的先验宽度下，估计器可使MSE降低近一个数量级。病变模拟表明，所提出的估计器具有比NCC、中值滤波NCC和先前的贝叶斯估计器更高的对比度噪声比，但对比度较低，非高斯先验形状比高斯先验具有更好的病变边缘分辨率。来自心脏射频消融ARFI成像数据集的体内结果表明，与NCC以及先前的贝叶斯估计器相比，病变对比度噪声比有定量改善。
