Learning a Variational Network for Reconstruction of Accelerated MRI Data
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Summary
To allow fast and high‐quality reconstruction of clinical accelerated multi‐coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning.
- Type
- article
- Published
- 2017-04-03
- Cited by
- 1,830
- References
- 64
- Access
- Open access
- OpenAlex
- https://openalex.org/W2604388535
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3815411
Keywords
Compressed sensing, Computer science, Acceleration, Gradient descent, Artificial intelligence
References
- Deep MRI brain extraction: A 3D convolutional neural network for skull stripping
- Compressed Sensing
- FlowNet: Learning Optical Flow with Convolutional Networks
- On learning optimized reaction diffusion processes for effective image restoration
- Total Generalized Variation
- An iteration formula for Fredholm integral equations of the first kind
- Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters.
- Certain Topics in Telegraph Transmission Theory
- SPIRiT: Iterative Self-consistent Parallel Imaging Reconstruction from Arbitrary k-Space
- Unsupervised texture segmentation using Gabor filters
- Ten Lectures on Wavelets
- A convergence analysis of the Landweber iteration for nonlinear ill-posed problems
- Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation
- A spatial data structure for fast Poisson-disk sample generation
- Sparse MRI: The application of compressed sensing for rapid MR imaging
- Nonlinear total variation based noise removal algorithms
- Generalized autocalibrating partially parallel acquisitions (GRAPPA)
- Second Order Total Generalized Variation (TGV) for MRI
- Parallel Imaging with Nonlinear Reconstruction using Variational Penalties
- ESPIRiT — An Eigenvalue Approach to Autocalibrating Parallel MRI: Where SENSE meets GRAPPA
Cited by
- Dynamic MRI using model-based deep learning and SToRM priors: MoDL-SToRM
- DIMENSION: Dynamic MR imaging with both k‐space and spatial prior knowledge obtained via multi‐supervised network training
- Image reconstruction by domain-transform manifold learning
- Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk
- Deep De-Aliasing for Fast Compressive Sensing MRI
- Insights into deep learning methods with application to cancer imaging
- Learned Primal-Dual Reconstruction
- Learned Experts' Assessment-based Reconstruction Network ("LEARN") for Sparse-data CT
- Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
- Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography
- Deep learning for undersampled MRI reconstruction
- A Transfer‐Learning Approach for Accelerated MRI Using Deep Neural Networks
- End-to-End Abnormality Detection in Medical Imaging
- Machine Learning in Radiology: Applications Beyond Image Interpretation.
- MoDL: Model Based Deep Learning Architecture for Inverse Problems
- DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
- MR Image Reconstruction Using Deep Density Priors
- Modern regularization methods for inverse problems
- Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery
- Solving Inverse Computational Imaging Problems Using Deep Pixel-Level Prior
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