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基于全反转式超声弹性成像的肝脏增强方法。

An Enhanced Method for Full-Inversion-Based Ultrasound Elastography of the Liver.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3887-3890. doi: 10.1109/EMBC48229.2022.9871656.

DOI:10.1109/EMBC48229.2022.9871656
PMID:36085977
Abstract

Similar to many other types of cancer, liver cancer is associated with biological changes that lead to tissue stiffening. An effective imaging technique that can be used for liver cancer detection through visualizing tissue stiffness is ultrasound elastography. In this paper, we show the effectiveness of an enhanced method of quasi-static ultrasound elastography for liver cancer assessment. The method utilizes initial estimates of axial and lateral displacement fields obtained using conventional time delay estimation (TDE) methods in conjunction with a recently proposed strain refinement algorithm to generate enhanced versions of the axial and lateral strain images. Another primary objective of this work is to investigate the sensitivity of the proposed method to the quality of these initial displacement estimates. The strain refinement algorithm is founded on the tissue mechanics principles of incompressibility and strain compatibility. Tissue strain images can serve as input for full-inversion-based elasticity image reconstruction algorithm. In this work, we use strain images generated by the proposed method with an iterative elasticity reconstruction algorithm. Ultrasound RF data collected from a tissue-mimicking phantom and in-vivo data of a liver cancer patient were used to evaluate the proposed method. Results show that while there is some sensitivity to the displacement field initial estimates, overall, the proposed method is robust to the quality of the initial estimates. Clinical Relevance- Improved elasticity images of the liver can aid in achieving more reliable diagnosis of liver cancer.

摘要

与许多其他类型的癌症一样,肝癌与导致组织变硬的生物学变化有关。超声弹性成像是一种有效的成像技术,可以通过可视化组织硬度来检测肝癌。在本文中,我们展示了增强型准静态超声弹性成像方法在肝癌评估中的有效性。该方法利用传统的时移估计(TDE)方法获得的轴向和横向位移场的初始估计,并结合最近提出的应变细化算法,生成轴向和横向应变图像的增强版本。这项工作的另一个主要目标是研究所提出方法对这些初始位移估计质量的敏感性。应变细化算法基于组织力学的不可压缩性和应变兼容性原理。组织应变图像可作为全反演弹性图像重建算法的输入。在这项工作中,我们使用所提出的方法生成的应变图像和迭代弹性重建算法。使用来自组织模拟体模的超声 RF 数据和肝癌患者的体内数据来评估所提出的方法。结果表明,虽然对位移场初始估计有一定的敏感性,但总体而言,该方法对初始估计的质量具有鲁棒性。临床意义- 肝脏的改进弹性图像可以帮助更可靠地诊断肝癌。

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