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 …
A Comprehensive Survey of Foundation Models in Medicine
Foundation models (FMs) are large-scale deep-learning models trained on extensive
datasets using self-supervised techniques. These models serve as a base for various …
datasets using self-supervised techniques. These models serve as a base for various …
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 …
Differentiable Score-Based Likelihoods: Learning CT Motion Compensation from Clean Images
Motion artifacts can compromise the diagnostic value of computed tomography (CT) images.
Motion correction approaches require a per-scan estimation of patient-specific motion …
Motion correction approaches require a per-scan estimation of patient-specific motion …
Accelerated, Robust Lower-Field Neonatal MRI with Generative Models
Neonatal Magnetic Resonance Imaging (MRI) enables non-invasive assessment of potential
brain abnormalities during the critical phase of early life development. Recently, interest has …
brain abnormalities during the critical phase of early life development. Recently, interest has …
[引用][C] DL-ZTE: Towards Deep Learning-based Methods for Dead Time Gap Recovery in Zero TE MRI
A Cismaru - 2024