# Deep learning in ultrasound imaging

## TL;DR

This paper reviews how deep learning can enhance ultrasound imaging by improving signal processing, adaptive techniques, and image quality, highlighting recent advances and potential impacts across the imaging pipeline.

## Contribution

It provides a comprehensive overview of deep learning applications in ultrasound, including novel methods for adaptive processing and structured signal recovery.

## Key findings

- Deep learning improves adaptive beamforming and spectral Doppler.
- Learned compressive encodings enhance color Doppler imaging.
- Frameworks for structured signal recovery enable super-resolution ultrasound.

## Abstract

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.