Deep learning in ultrasound imaging
This work addresses improving ultrasound imaging quality and efficiency for medical diagnostics, but it is incremental as it builds on existing deep learning and signal processing techniques.
The paper tackles the integration of deep learning across ultrasound imaging systems, from signal acquisition to advanced applications, by proposing methods like adaptive beamforming and compressive encodings, resulting in efficient solutions for tasks such as clutter suppression and super-resolution.
We consider deep learning strategies in ultrasound systems, from the front-end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, we discuss methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g. sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage. As some examples, we outline efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler through artificial agents, learn compressive encodings for color Doppler, and provide a framework for structured signal recovery by learning fast approximations of iterative minimization problems, with applications to clutter suppression and super-resolution ultrasound. These emerging technologies may have considerable impact on ultrasound imaging, showing promise across key components in the receive processing chain.