Deep learning for retrospective motion correction in MRI: a comprehensive review
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 …
the MR signal is acquired in frequency space, any motion of the imaged object leads to …
[HTML][HTML] Stacked U-Nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI
MA Al-Masni, S Lee, J Yi, S Kim, SM Gho, YH Choi… - NeuroImage, 2022 - Elsevier
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 …
movement due to the relatively long data acquisition time. This could cause severe …
Unpaired MR motion artifact deep learning using outlier-rejecting bootstrap aggregation
Recently, deep learning approaches for MR motion artifact correction have been extensively
studied. Although these approaches have shown high performance and lower …
studied. Although these approaches have shown high performance and lower …
Stop moving: MR motion correction as an opportunity for artificial intelligence
Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can
seriously deteriorate the image quality. Various prospective and retrospective methods have …
seriously deteriorate the image quality. Various prospective and retrospective methods have …
Unsupervised dual-domain disentangled network for removal of rigid motion artifacts in MRI
B Wu, C Li, J Zhang, H Lai, Q Feng, M Huang - Computers in Biology and …, 2023 - Elsevier
Motion artifacts in magnetic resonance imaging (MRI) have always been a serious issue
because they can affect subsequent diagnosis and treatment. Supervised deep learning …
because they can affect subsequent diagnosis and treatment. Supervised deep learning …
Retrospective respiratory motion correction in cardiac cine MRI reconstruction using adversarial autoencoder and unsupervised learning
The aim of this study was to develop a deep neural network for respiratory motion
compensation in free‐breathing cine MRI and evaluate its performance. An adversarial …
compensation in free‐breathing cine MRI and evaluate its performance. An adversarial …
A knowledge interaction learning for multi-echo MRI motion artifact correction towards better enhancement of SWI
MA Al-Masni, S Lee, AK Al-Shamiri, SM Gho… - Computers in biology …, 2023 - Elsevier
Abstract Patient movement during Magnetic Resonance Imaging (MRI) scan can cause
severe degradation of image quality. In Susceptibility Weighted Imaging (SWI), several …
severe degradation of image quality. In Susceptibility Weighted Imaging (SWI), several …
Annealed score-based diffusion model for mr motion artifact reduction
Motion artifact reduction is one of the important research topics in MR imaging, as the motion
artifact degrades image quality and makes diagnosis difficult. Recently, many deep learning …
artifact degrades image quality and makes diagnosis difficult. Recently, many deep learning …
Retrospective motion correction for preclinical/clinical magnetic resonance imaging based on a conditional generative adversarial network with entropy loss
Q Bao, Y Chen, C Bai, P Li, K Liu, Z Li… - NMR in …, 2022 - Wiley Online Library
Multishot scan magnetic resonance imaging (MRI) acquisition is inherently sensitive to
motion, and motion artifact reduction is essential for improving the image quality in MRI. This …
motion, and motion artifact reduction is essential for improving the image quality in MRI. This …
Temporally aware volumetric generative adversarial network‐based MR image reconstruction with simultaneous respiratory motion compensation: Initial feasibility in …
Purpose Develop a novel three‐dimensional (3D) generative adversarial network (GAN)‐
based technique for simultaneous image reconstruction and respiratory motion …
based technique for simultaneous image reconstruction and respiratory motion …