Medical ultrasound imaging is an ongoing research field for digital signal processing. Following decades of developement in the analogue domain, the introduction of high power computation has led to increased activity and research in the fields of digital signal processing, and, most recently, in machine learning, for the sake of delivering higher quality imaging, while reducing the size of the data required to acquire, process, display and, recently, wirelessly transmit the data to remote devices. An overview of the basics of ultrasound acquisition and imaging is given, followed by a presentation of its limitations and challenges. An in-depth explanation about the development and algorithmics of the methods designed to address each of these challenges follows, with an introduction of the outstanding tasks and issues posed in each field, and their potential applications and benefits to modern healthcare.

The multi-billion dollar, worldwide medical ultrasound (US) market continues to grow annually. Its non-ionizing nature, real-time capabilities and relatively low cost, compared to other imaging modalities, have led to significant applications in many different fields, including cardiology, angiology, obstetrics and emergency medicine. Facilitated by ongoing innovations, US continues to change rules and norms regarding patient screening, diagnosis and surgery. This huge and promising market is constantly driven by new imaging and processing techniques. From 3D images to sophisticated software, hardware and portability improvements, it is clear that the status of US as one of the leading medical imaging technologies is ensured for many years ahead. However, as imaging systems evolve, new engineering challenges emerge. Acquisition, transmission and processing of huge amounts of data are common for all ultrasound-based imaging modalities. Moreover, achieving higher resolution is constantly on demand, as improved diagnosis could be achieved by better visualization of organs and blood vessels deep within tissues. In this article, our goal is to motivate further interest and research in emerging processing techniques, as well as their applications in medical ultrasound, enabled by recent advancements in signal processing algorithms and deep learning. We address some of the primary challenges and potential remedies from a signal processing perspective, by exploiting the inherent structure of the received US signal.

Adaptive beamforming can lead to substantial improvement in resolution and contrast of ultrasound images over standard delay and sum beamforming. Here we introduce the adaptive time-channel (ATC) beamformer, a data-driven approach that combines spatial and temporal information simultaneously, thus generalizing minimum variance beamformers. Moreover, we broaden the concept of apodization to the temporal dimension. Our approach reduces noises by allowing for the weights to adapt in both the temporal and spatial dimensions, thereby reducing artifacts caused by the media's inhomogeneities. We apply our method to in-silico data and show 12% resolution enhancement along with 2-fold contrast improvement, and significant noise reduction with respect to delay and sum and minimum variance beamformers.

Ultra-fast ultrasound imaging relies on coherent Plane Wave (PW) compounding to obtain sufficient spatial resolution, and contrast. However, the process of coherent PW compounding incurs a loss in temporal resolution. We propose a Deep Learning (DL) network that achieves high resolution PW compounding using a reduced number of PW transmits. We embed a model based signal processing algorithm in the design of the network, which leads to better performance through the exploitation of the prior information that is now available to the network. Our proposed method outperforms two benchmark networks, yielding approximately an 8.2% improvement in PSNR, over the next best network. Aiming for an additional boost in resolution, we moreover train towards images acquired using higher transmit frequencies.