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

Purpose To determine the model performance of artificial intelligence (AI) in detecting rotator
cuff pathology using different imaging modalities and to compare capability with physicians …

Background MRI is a powerful diagnostic tool with a long acquisition time. Recently, deep
learning (DL) methods have provided accelerated high-quality image reconstructions from …

Objectives To compare the image quality and diagnostic performance of conventional
motion-corrected periodically rotated overlapping parallel line with enhanced reconstruction …

Background Deep learning (DL)–based MRI reconstructions can reduce imaging times for
turbo spin-echo (TSE) examinations. However, studies that prospectively use DL-based …

Objective To investigate the feasibility of deep learning–based MRI (DL-MRI) in its
application in shoulder imaging and compare its performance with conventional MR imaging …

Introduction Deep learning–based MRI reconstruction has recently been introduced to
improve image quality. This study aimed to evaluate the performance of deep learning …

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 …

Background Artificial intelligence (AI) aims to simulate human intelligence using automated
computer algorithms. There has been a rapid increase in research applying AI to various …

Objectives: To assess diagnostic performance of standard radial k-space (PROPELLER)
MRI sequences and compare with accelerated acquisitions combined with a deep learning …