Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and
a gradual sampling process to synthesize data, have gained increasing research interest …

Diffusion models have recently gained traction as a powerful class of deep generative priors,
excelling in a wide range of image restoration tasks due to their exceptional ability to model …

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

Abstract Magnetic Resonance Imaging (MRI) serves as a critical tool in modern medical
diagnosis, and its accuracy is directly linked to patient treatment and recovery. However …

Magnetic resonance imaging is an important non-invasive diagnostic tool, yet is vulnerable
to motion artifacts due to the long acquisition time. With the recent development of deep …

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 …

Deep learning (DL) methods have been extensively applied to various image recovery
problems, including magnetic resonance imaging (MRI) and computed tomography (CT) …

Magnetic Resonance Imaging (MRI) stands as a powerful modality in clinical diagnosis.
However, it is known that MRI faces challenges such as long acquisition time and …

Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with
clinical diagnosis. Removing motion artifacts is a straightforward solution and has been …

Abstract Magnetic Resonance Image (MRI) is a powerful medical imaging modality with non-
ionizing radiation. However, due to its long scanning time, patient movement is prone to …