Compressed sensing (CS) has been playing a key role in accelerating the magnetic
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …

This article provides a focused overview of emerging technology in musculoskeletal MRI
and CT. These technological advances have primarily focused on decreasing examination …

Magnetic resonance imaging (MRI) is the keystone of modern musculoskeletal imaging;
however, long pulse sequence acquisition times may restrict patient tolerability and access …

Deep learning based generative adversarial networks (GAN) can effectively perform image
reconstruction with under-sampled MR data. In general, a large number of training samples …

The 3D nature and soft-tissue contrast of MRI makes it an invaluable tool for osteoarthritis
research, by facilitating the elucidation of disease pathogenesis and progression. The recent …

The anterior cruciate ligaments (ACL) are the fundamental structures in preserving the
common biomechanics of the knees and most frequently damaged knee ligaments. An ACL …

MRI is an expensive and traditionally time‐intensive modality in imaging. With the paradigm
shift toward value‐based healthcare, radiology departments must examine the entire MRI …

Purpose To construct and validate a nomogram model that integrated deep learning
radiomic features based on multiparametric MRI and clinical features for risk stratification of …

Three-dimensional (3D) magnetic resonance imaging (MRI) of the knee is widely used in
musculoskeletal (MSK) imaging. Currently, 3D sequences are most commonly used for …

Objective: The objective of this study was to evaluate a deep learning (DL) reconstruction for
turbo spin echo (TSE) sequences of the elbow regarding image quality and visualization of …