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Extracting information from sonoelastographic images.

机译：从超声弹性成像图像中提取信息。

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This thesis focuses on the implementation of image processing tools to extract information from images acquired with sonoelastography and crawling wave (CrW) sonoelastography. These algorithms enhance the quality of the images; extract location and size information of discrete lesions; and provide viscoelastic properties of the imaged tissue. Among the implemented tools, a semi-automated segmentation algorithm to measure discrete lesions in sonoelastographic images is proposed and evaluated through simulations, and experiments ex vivo and in vivo. The algorithm reduces variability and processing time in the measurements while keeping results comparable to manual segmentation. A second algorithm to process CrW images is implemented to extract shear velocity information from homogenous tissues. This correlation-based algorithm is successfully applied to the measurement of viscoelastic properties of human prostate ex vivo. Finally, motion filtering and slow time processing are introduced to enhance the quality of the CrW images by exploiting their temporal and spatial harmonic properties. The proposed tools are applied to two important clinical applications: prostate cancer detection and measurement of thermally ablated lesions in liver. In the former application, sonoelastography has an accuracy of over 80% for finding tumors larger than 4 mm in diameter, both in vivo and ex vivo, and slightly underestimates their volumes. CrW sonoelastography estimates the shear velocities of cancerous and normal prostate tissue as 4.75+/-0.97 m/s and 3.26+/-0.87 m/s, respectively. In the latter clinical application, results suggest that sonoelastography has the potential to be used as a complementary technique to conventional ultrasound for monitoring thermal ablation and follow-up imaging.
机译：本文着重于图像处理工具的实现，以从超声弹性成像和爬行波（CrW）超声弹性成像获取的图像中提取信息。这些算法提高了图像质量。提取离散病变的位置和大小信息；并提供成像组织的粘弹性。在已实现的工具中，提出了一种用于测量超声弹性成像图像中离散损伤的半自动分割算法，并通过模拟以及离体和体内实验进行了评估。该算法减少了测量中的可变性和处理时间，同时保持了与手动分割相当的结果。实现了处理CrW图像的第二种算法，以从均质组织中提取剪切速度信息。这种基于相关性的算法已成功应用于离体人体前列腺的粘弹性的测量。最后，引入运动滤波和慢时间处理以通过利用CrW图像的时间和空间谐波特性来提高其质量。拟议的工具应用于两个重要的临床应用：前列腺癌的检测和肝脏热消融病变的测量。在前一种应用中，超声弹性成像在体内和离体中发现直径大于4毫米的肿瘤的准确率均超过80％，并略微低估了它们的体积。 CrW超声弹性成像估计癌组织和正常前列腺组织的剪切速度分别为4.75 +/- 0.97 m / s和3.26 +/- 0.87 m / s。在后一种临床应用中，结果表明，超声弹性成像有可能被用作常规超声的补充技术，以监测热消融和随访影像。

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