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Found 6 papers, 4 papers with code
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
However, they require several iterations of a large neural network to handle high-dimensional imaging tasks such as 3D MRI.
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation
While recent machine learning methods for MRI reconstruction and analysis have shown promise for reducing this burden, these techniques are primarily validated with imperfect image quality metrics, which are discordant with clinically-relevant measures that ultimately hamper clinical deployment and clinician trust.
VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction
Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstruction.
SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric
Self-supervised learning (SSL) has become a popular pre-training tool due to its ability to capture generalizable and domain-specific feature representations of the underlying data for downstream tasks.
Greedy Learning for Large-Scale Neural MRI Reconstruction
Model-based deep learning approaches have recently shown state-of-the-art performance for accelerated MRI reconstruction.
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning
Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction.
