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 …

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 …

Abstract Magnetic Resonance Imaging (MRI) is sensitive to motion caused by patient
movement due to the relatively long data acquisition time. This could cause severe …

Abstract Patient movement during Magnetic Resonance Imaging (MRI) scan can cause
severe degradation of image quality. In Susceptibility Weighted Imaging (SWI), several …

Existing deep learning (DL)-based magnetic resonance imaging (MRI) retrospective motion
correction (MoCo) models are typically task-specific, which makes them challenging to …

We propose PHIMO, a physics-informed learning-based motion correction method tailored
to quantitative MRI. PHIMO leverages information from the signal evolution to exclude …

Magnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue
contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for …

In this work, we propose a realistic, physics-aware motion simulation procedure for T 2*-
weighted magnetic resonance imaging (MRI) to improve learning-based motion correction …

MRI, a widespread non-invasive medical imaging modality, is highly sensitive to patient
motion. Despite many attempts over the years, motion correction remains a difficult problem …

T2*-weighted gradient echo MR imaging is strongly impacted by subject head motion due to
motion-related changes in B0 inhomogeneities. Within the oxygenation-sensitive mqBOLD …