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 …

Flexible sensors, made from flexible electronic materials, are of great importance in the
medical field due to the rising prevalence of cardiovascular and cerebrovascular diseases …

Background High‐resolution magnetic resonance imaging (MRI) with excellent soft‐tissue
contrast is a valuable tool utilized for diagnosis and prognosis. However, MRI sequences …

Objective. This study developed an unsupervised motion artifact reduction method for
magnetic resonance imaging (MRI) images of patients with brain tumors. The proposed …

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

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 …

Deep learning presents a generalizable solution for motion correction requiring no pulse
sequence modifications or additional hardware, but previous networks have all been …

This study aimed to remove motion artifacts from brain magnetic resonance (MR) images
using a U-Net model. In addition, a simulation method was proposed to increase the size of …

Purpose The aim of this study was to develop a reconstruction method that more fully
models the signals and reconstructs gradient echo (GRE) images without sacrificing the …