Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstruction
Not on the map. Why?
Abstract (arXiv)
Magnetic Resonance Imaging (MRI) is a widely used imaging technique, however it has the limitation of long scanning time. Though previous model-based and learning-based MRI reconstruction methods have shown promising performance, most of them have not fully utilized the edge prior of MR images, and there is still much room for improvement. In this paper, we build a joint edge optimization model that not only incorporates individual regularizers specific to both the MR image and the edges, but also enforces a co-regularizer to effectively establish a stronger correlation between them. Specifically, the edge information is defined through a non-edge probability map to guide the image reconstruction during the optimization process. Meanwhile, the regularizers pertaining to images and edges are incorporated into a deep unfolding network to automatically learn their respective inherent a-priori information.Numerical experiments, consisting of multi-coil and single-coil MRI data with different sampling schemes at a variety of sampling factors, demonstrate that the proposed method outperforms other compared methods.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 25 of its 71 references that are in the map. First posted on arXiv 9 May 2024.
Builds on
25 of 71 references.
References · 25
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
- 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
- 2019Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processingcited by 411
- 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 384
- 2017Compressed Sensing using Generative Modelscited by 337
- 2021Score-based diffusion models for accelerated MRIcited by 308
- 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
- 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 231
- 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 201
13 more
- 2017Deep learning for undersampled MRI reconstructioncited by 91
- 2017Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Losscited by 82
- 2018Denoising Prior Driven Deep Neural Network for Image Restorationcited by 80
- 2022A Neural-Network-Based Convex Regularizer for Inverse Problemscited by 40
- 2021Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstructioncited by 34
- 2019VS-Net: Variable splitting network for accelerated parallel MRI reconstructioncited by 31
- 2021Eformer: Edge Enhancement based Transformer for Medical Image Denoisingcited by 30
- 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
- 2019IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRIcited by 13
- 2021High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Losscited by 6
- 2021Learned Half-Quadratic Splitting Network for MR Image Reconstructioncited by 5
- 2023Fast MRI Reconstruction via Edge Attentioncited by 3
- 2022Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstructioncited by 2
Built on by
Citing papers per year: 2025: 2.
2 papers.