Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …

Large neural networks excel in many domains, but they are expensive to train and fine-tune.
A popular approach to reduce their compute or memory requirements is to replace dense …

Image classification is the act of labeling groups of pixels or voxels of an image based on
some rules. It finds applications in medical image analysis, and satellite image identification …

Background Deep learning (DL)–based MRI reconstructions can reduce examination times
for turbo spin-echo (TSE) acquisitions. Studies that prospectively employ DL-based …

More widespread use of positron emission tomography (PET) imaging is limited by its high
cost and radiation dose. Reductions in PET scan time or radiotracer dosage typically …

Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However,
long image acquisition times, the need for qualitative expert analysis, and the lack of (and …

Purpose To develop a method for building MRI reconstruction neural networks robust to
changes in signal‐to‐noise ratio (SNR) and trainable with a limited number of fully sampled …

Purpose To organize a multi-institute knee MRI segmentation challenge for characterizing
the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring …

Background Deep learning (DL)–accelerated MRI can substantially reduce examination
times. However, studies prospectively evaluating the diagnostic performance of DL …

Please see the Editorial Comment by Derik L. Davis discussing this article. BACKGROUND.
Potential approaches for abbreviated knee MRI, including prospective acceleration with …