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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 …
Deep learning for accelerated and robust MRI reconstruction
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
Deep learning for accelerated and robust MRI reconstruction: a review
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
Physics-informed deep learning for motion-corrected reconstruction of quantitative brain MRI
We propose PHIMO, a physics-informed learning-based motion correction method tailored
to quantitative MRI. PHIMO leverages information from the signal evolution to exclude …
to quantitative MRI. PHIMO leverages information from the signal evolution to exclude …
SISMIK for brain MRI: Deep-learning-based motion estimation and model-based motion correction in k-space
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 …
motion. Despite many attempts over the years, motion correction remains a difficult problem …
MRI Motion Correction Through Disentangled CycleGAN Based on Multi-Mask K-Space Subsampling
This work proposes a new retrospective motion correction method, termed DCGAN-MS,
which employs disentangled CycleGAN based onmulti-mask k-space subsampling (DCGAN …
which employs disentangled CycleGAN based onmulti-mask k-space subsampling (DCGAN …
IM-MoCo: Self-supervised MRI Motion Correction Using Motion-Guided Implicit Neural Representations
Abstract Motion artifacts in Magnetic Resonance Imaging (MRI) arise due to relatively long
acquisition times and can compromise the clinical utility of acquired images. Traditional …
acquisition times and can compromise the clinical utility of acquired images. Traditional …
IM-MoCo: Self-supervised MRI Motion Correction using Motion-Guided Implicit Neural Representations
Motion artifacts in Magnetic Resonance Imaging (MRI) arise due to relatively long
acquisition times and can compromise the clinical utility of acquired images. Traditional …
acquisition times and can compromise the clinical utility of acquired images. Traditional …