This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for
the reconstruction of images in positron emission tomography (PET). Deep learning can be …

In magnetic resonance imaging (MRI), inhomogeneity in the main magnetic field used for
imaging, referred to as off‐resonance, can lead to image artifacts ranging from mild to …

Purpose To develop a strategy for training a physics‐guided MRI reconstruction neural
network without a database of fully sampled data sets. Methods Self‐supervised learning via …

This monograph covers some recent advances in a range of acceleration techniques
frequently used in convex optimization. We first use quadratic optimization problems to …

Magnetic resonance imaging (MRI) is a noninvasive diagnostic tool that provides excellent
soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging …

Physics-driven deep learning methods have emerged as a powerful tool for computational
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …

Low-field MRI scanners are significantly less expensive than their high-field counterparts,
which gives them the potential to make MRI technology more accessible all around the …

Inverse problems for accelerated MRI typically incorporate domain-specific knowledge
about the forward encoding operator in a regularized reconstruction framework. Recently …

Deep learning (DL) has emerged as a powerful tool for accelerated MRI reconstruction, but
these methods often necessitate a database of fully-sampled measurements for training …

Deep learning (DL) has emerged as a tool for improving accelerated MRI reconstruction. A
common strategy among DL methods is the physics-based approach, where a regularized …