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Found 4 papers, 1 papers with code
Deep learning-based reconstruction of highly accelerated 3D MRI
The trained model was evaluated on 3D MPRAGE brain scan data retrospectively-undersampled with a 10-fold acceleration, compared to a conventional parallel imaging method with a 2-fold acceleration.
Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation
Recent accelerated MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning.
Generative Adversarial Network
Image-to-Image Translation
+2
A Novel Approach for Correcting Multiple Discrete Rigid In-Plane Motions Artefacts in MRI Scans
Motion artefacts created by patient motion during an MRI scan occur frequently in practice, often rendering the scans clinically unusable and requiring a re-scan.
Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction
Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning.
