Ultrasound Signal Processing: From Models to Deep Learning

Abstract

Medical ultrasound imaging relies heavily on high-quality signal processing algorithms to provide reliable and interpretable image reconstructions. Hand-crafted reconstruction methods, often based on approximations of the underlying measurement model, are useful in practice, but notoriously fall behind in terms of image quality. 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 have gained popularity, which are optimized in a datadriven fashion. These model-agnostic methods 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 datadriven deep learning, as well as exploiting domain knowledge.