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 …

In the context of sensor-based data analysis, the compensation of image artifacts is a
challenge. When the structures of interest are not clearly visible in an image, algorithms that …

Fluorescent imaging has revolutionized biomedical research, enabling the study of intricate
cellular processes. Multiplex immunofluorescent imaging has extended this capability …

Purpose To use deep learning for suppression of the artifact‐generating T1‐relaxation echo
in cine displacement encoding with stimulated echoes (DENSE) for the purpose of reducing …

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 …

Motion artifacts in magnetic resonance imaging (MRI) are one of the issues that can affect
diagnosis. To remove this motion artifact from MRI, we propose a modified restricted …

Deep learning has attracted a great amount of interest in recent years and has become a
rapidly emerging field in artificial intelligence. In medical image analysis, deep learning …

In-scanner head motion often leads to degradation in MRI scans and is a major source of
error in diagnosing brain abnormalities. Researchers have explored various approaches …

Background: The appearance of Noise Artifacts is admittedly very disturbing the quality of
MRI diagnostic images. The application of BLADE and STIR sequences based on artificial …

Machine learning (ML) and deep learning (DL) have been widely used for the computer-
aided diagnosis (CAD) of neurodegenerative diseases The main limitation of these tools is …