IEEE Trans Ultrason Ferroelectr Freq Control. 2023 Sep;70(9):909-919. doi: 10.1109/TUFFC.2023.3255843. Epub 2023 Aug 29.
Ultrasound (US) imaging is a paramount modality in many image-guided surgeries and percutaneous interventions, thanks to its high portability, temporal resolution, and cost-efficiency. However, due to its imaging principles, the US is often noisy and difficult to interpret. Appropriate image processing can greatly enhance the applicability of the imaging modality in clinical practice. Compared with the classic iterative optimization and machine learning (ML) approach, deep learning (DL) algorithms have shown great performance in terms of accuracy and efficiency for US processing. In this work, we conduct a comprehensive review on deep-learning algorithms in the applications of US-guided interventions, summarize the current trends, and suggest future directions on the topic.
超声（US）成像在许多影像引导手术和经皮介入中是一种至关重要的方式，这要归功于其高便携性、时间分辨率和成本效益。然而，由于其成像原理，US 通常噪声大且难以解释。适当的图像处理可以极大地提高成像方式在临床实践中的适用性。与经典的迭代优化和机器学习（ML）方法相比，深度学习（DL）算法在 US 处理的准确性和效率方面表现出了巨大的性能。在这项工作中，我们对 US 引导介入应用中的深度学习算法进行了全面的回顾，总结了当前的趋势，并就该主题提出了未来的方向。
