Kheirkhah Niusha, Dempsey Sergio C H, Rivaz Hassan, Samani Abbas, Sadeghi-Naini Ali
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2051-2054. doi: 10.1109/EMBC44109.2020.9175869.
Cancer is known to induce significant structural changes to tissue. In most cancers, including breast cancer, such changes yield tissue stiffening. As such, imaging tissue stiffness can be used effectively for cancer diagnosis. One such imaging technique, ultrasound elastography, has emerged with the aim of providing a low-cost imaging modality for effective breast cancer diagnosis. In quasi-static breast ultrasound elastography, the breast is stimulated by ultrasound probe, leading to tissue deformation. The tissue displacement data can be estimated using a pair of acquired ultrasound radiofrequency (RF) data pertaining to pre- and post-deformation states. The data can then be used within a mathematical framework to construct an image of the tissue stiffness distribution. Ultrasound RF data is known to include significant noise which lead to corruption of estimated displacement fields, especially the lateral displacements. In this study, we propose a tissue mechanics-based method aiming at improving the quality of estimated displacement data. We applied the method to RF data acquired from a tissue-mimicking phantom. The results indicated that the method is effective in improving the quality of the displacement data.
众所周知，癌症会导致组织发生显著的结构变化。在包括乳腺癌在内的大多数癌症中，此类变化会使组织变硬。因此，对组织硬度进行成像可有效用于癌症诊断。一种这样的成像技术，即超声弹性成像，已应运而生，旨在提供一种低成本的成像方式用于有效的乳腺癌诊断。在准静态乳腺超声弹性成像中，乳房由超声探头刺激，导致组织变形。可以使用一对与变形前和变形后状态相关的采集到的超声射频（RF）数据来估计组织位移数据。然后，这些数据可在一个数学框架内用于构建组织硬度分布图像。众所周知，超声RF数据包含大量噪声，这会导致估计的位移场失真，尤其是横向位移。在本研究中，我们提出了一种基于组织力学的方法，旨在提高估计位移数据的质量。我们将该方法应用于从组织模拟体模获取的RF数据。结果表明该方法在提高位移数据质量方面是有效的。
