Compressed Sensing MRI Using a Recursive Dilated Network
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Summary
This work proposes a recursive dilated network (RDN) for CS-MRI that achieves good performance while reducing the number of network parameters, and adopts dilated convolutions in each recursive block to aggregate multi-scale information within the MRI.
- Type
- article
- Published
- 2018-04-26
- Cited by
- 87
- References
- 29
- Access
- Open access
- OpenAlex
- https://openalex.org/W2787943924
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:19151693
Keywords
Computer science, Compressed sensing, Magnetic resonance imaging, Block (permutation group theory), Algorithm
References
- Compressed Sensing
- Efficient MR Image Reconstruction for Compressed MR Imaging
- Recurrent convolutional neural network for object recognition
- Magnetic resonance image reconstruction using trained geometric directions in 2D redundant wavelets domain and non-convex optimization.
- Compressive Sensing via Nonlocal Low-Rank Regularization
- Undersampled MRI reconstruction with patch-based directional wavelets.
- Compressive Sensing MRI with Wavelet Tree Sparsity
- Sparse MRI: The application of compressed sensing for rapid MR imaging
- Convolutional-Recursive Deep Learning for 3D Object Classification
- Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI
- Image quality assessment: from error visibility to structural similarity
- Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- A Fast Alternating Direction Method for TVL1-L2 Signal Reconstruction From Partial Fourier Data
- An efficient algorithm for compressed MR imaging using total variation and wavelets
- MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning
- Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform
- Deep Residual Learning for Image Recognition
- Deeply-Recursive Convolutional Network for Image Super-Resolution
- Decoupled Algorithm for MRI Reconstruction Using Nonlocal Block Matching Model: BM3D-MRI
Cited by
- DIMENSION: Dynamic MR imaging with both k‐space and spatial prior knowledge obtained via multi‐supervised network training
- CRDN: Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint
- A Very Deep Densely Connected Network for Compressed Sensing MRI
- Adversarial training and dilated convolutions for compressed sensing MRI
- Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction
- A comparative study of CNN-based super-resolution methods in MRI reconstruction and its beyond
- lambda-Net: Reconstruct Hyperspectral Images From a Snapshot Measurement
- MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning
- An Unsupervised Deep Learning Method for Parallel Cardiac MRI via Time-Interleaved Sampling
- An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection
- DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
- Discernible Compressed Images via Deep Perception Consistency
- Geometric Approaches to Increase the Expressivity of Deep Neural Networks for MR Reconstruction
- A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography.
- Fast Reconstruction of 1D Compressive Sensing Data Using a Deep Neural Network
- Deep Low-rank Prior in Dynamic MR Imaging
- IKWI-net: A cross-domain convolutional neural network for undersampled magnetic resonance image reconstruction.
- 2D probabilistic undersampling pattern optimization for MR image reconstruction
- LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small dataset
- An unsupervised deep learning method for multi-coil cine MRI
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