TY - JOUR
T1 - Ultrasound Signal Processing
T2 - From Models to Deep Learning
AU - Luijten, Ben
AU - Chennakeshava, Nishith
AU - Eldar, Yonina C.
AU - Mischi, Massimo
AU - van Sloun, Ruud J.G.
PY - 2022/4/9
Y1 - 2022/4/9
N2 - 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 data-driven 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 data-driven deep learning, as well as exploiting domain knowledge. These model-based solutions yield high robustness, and require less trainable parameters and training data than conventional neural networks. In this work we provide an overview of these methods from the 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 these model-based deep learning techniques for medical ultrasound applications.
AB - 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 data-driven 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 data-driven deep learning, as well as exploiting domain knowledge. These model-based solutions yield high robustness, and require less trainable parameters and training data than conventional neural networks. In this work we provide an overview of these methods from the 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 these model-based deep learning techniques for medical ultrasound applications.
KW - Ultrasound
KW - Deep Learning
KW - Model based methods
KW - Beamforming
U2 - 10.48550/arXiv.2204.04466
DO - 10.48550/arXiv.2204.04466
M3 - Article
VL - 2022
JO - arXiv
JF - arXiv
M1 - 2204.04466
ER -