Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …

This paper tries to give a gentle introduction to deep learning in medical image processing,
proceeding from theoretical foundations to applications. We first discuss general reasons for …

Foundation large language models (LLMs) have shown an impressive ability to solve tasks
across a wide range of fields including health. To effectively solve personalized health tasks …

In this article, 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 …

Medical ultrasound images are reconstructed with simplifying assumptions on wave
propagation, with one of the most prominent assumptions being that the imaging medium is …

Phase-contrast magnetic resonance imaging (MRI) provides time-resolved quantification of
blood flow dynamics that can aid clinical diagnosis. Long in vivo scan times due to repeated …

In this article, we perform a review of the state-of-the-art of hybrid machine learning in
medical imaging. We start with a short summary of the general developments of the past in …

Background Volumetric quantification of mean and fluctuating velocity components of
transient and turbulent flows promises a comprehensive characterization of valvular and …

Ultrasound images are distorted by phase aberration arising from local sound speed
variations in the tissue, which lead to inaccurate time delays in beamforming and loss of …

Medical ultrasound imaging relies heavily on high-quality signal processing to provide
reliable and interpretable image reconstructions. Conventionally, reconstruction algorithms …