The role of physicians has always been to synthesize the data available to them to identify
diagnostic patterns that guide treatment and follow response. Today, increasingly …

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

Artificial intelligence algorithms based on principles of deep learning (DL) have made a
large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the …

Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since
the MR signal is acquired in frequency space, any motion of the imaged object leads to …

Head motion during MRI acquisition presents significant challenges for neuroimaging
analyses. In this work, we present a retrospective motion correction framework built on a …

Physiological and physical motions of the subjects, eg, patients, are the primary sources of
image artifacts in magnetic resonance imaging (MRI), causing geometric distortion, blurring …

Learning-based translation between MRI contrasts involves supervised deep models trained
using high-quality source-and target-contrast images derived from fully-sampled …

Motion artifacts deteriorate the quality of magnetic resonance (MR) images. This study
proposes a new method to detect phase-encoding (PE) lines corrupted by motion and …

The suppression of motion artefacts from MR images is a challenging task. The purpose of
this paper was to develop a standalone novel technique to suppress motion artefacts in MR …

Background Three-dimensional, whole heart, balanced steady state free precession (WH-
bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However …