Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Conventionally, reconstruction algorithms where derived from physical principles. These algorithms rely on assumptions and approximations of the underlying measurement model, limiting image quality in settings were these assumptions break down. Conversely, more sophisticated solutions based on statistical modelling, careful parameter tuning, or through increased model complexity, can be sensitive to different environments. Recently, deep learning based methods, which are optimized in a data-driven fashion, have gained popularity. These model-agnostic techniques often rely on generic model structures, and require vast training data to converge to a robust solution. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge. These model-based solutions yield high robustness, and require less parameters and training data than conventional neural networks. In this work we provide an overview of these techniques from recent literature, and discuss a wide variety of ultrasound applications. We aim to inspire the reader to further research in this area, and to address the opportunities within the field of ultrasound signal processing. We conclude with a future perspective on model-based deep learning techniques for medical ultrasound.

翻译：医学超声成像高度依赖高质量信号处理，以提供可靠且可解释的图像重建。传统上，重建算法源于物理原理。这些算法依赖于对基础测量模型的假设与近似，在假设失效的场景下会限制图像质量。相反，基于统计建模、精细参数调优或增加模型复杂度的更复杂方案，可能对不同环境敏感。近年来，以数据驱动方式优化的深度学习方法日益普及。这些与模型无关的技术通常依赖通用模型结构，需海量训练数据才能收敛至稳健解。一种相对较新的范式结合了两者优势：利用数据驱动的深度学习，同时挖掘领域知识。此类基于模型的方法具有高鲁棒性，且相比传统神经网络所需参数和训练数据更少。本文概述了近期文献中的相关技术，并探讨了广泛超声应用场景。我们旨在激发读者对该领域的进一步研究，并把握超声信号处理领域的机遇。最后，我们对医学超声中基于模型的深度学习技术进行了未来展望。