MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction
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Abstract (arXiv)
Deep unfolding networks (DUNs) have demonstrated significant potential in accelerating magnetic resonance imaging (MRI). However, they often encounter high computational costs and slow convergence rates. Besides, they struggle to fully exploit the complementarity when incorporating multiple priors. In this study, we propose a multi-prior collaborative DUN, termed MCU-Net, to address these limitations. Our method features a parallel structure consisting of different optimization-inspired subnetworks based on low-rank and sparsity, respectively. We design a gates-controlled spatial attention module (GSAM), evaluating the relative confidence (RC) and overall confidence (OC) maps for intermediate reconstructions produced by different subnetworks. RC allocates greater weights to the image regions where each subnetwork excels, enabling precise element-wise collaboration. We design correction modules to enhance the effectiveness in regions where both subnetworks exhibit limited performance, as indicated by low OC values, thereby obviating the need for additional branches. The gate units within GSAMs are designed to preserve necessary information across multiple iterations, improving the accuracy of the learned confidence maps and enhancing robustness against accumulated errors. Experimental results on multiple datasets show significant improvements on PSNR and SSIM results with relatively low FLOPs compared to cutting-edge methods. Additionally, the proposed strategy can be conveniently applied to various DUN structures to enhance their performance.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 48 of its 152 references that are in the map. First posted on arXiv 4 Feb 2024.
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References · 48
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
- 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
- 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
- 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 433
- 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 387
- 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 342
- 2021Score-based diffusion models for accelerated MRIcited by 311
- 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 286
- 2020Deep Unfolding Network for Image Super-Resolutioncited by 254
- 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 202
- 2022Deep Generalized Unfolding Networks for Image Restorationcited by 121
36 more
- 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 118
- 2022Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imagingcited by 96
- 2017Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Losscited by 82
- 2017A Deep Cascade of Convolutional Neural Networks for MR Image Reconstructioncited by 74
- 2022Swin Transformer for Fast MRIcited by 56
- 2022ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformercited by 52
- 2017Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detectioncited by 46
- 2022High-Frequency Space Diffusion Models for Accelerated MRIcited by 42
- 2015Loss Functions for Neural Networks for Image Processingcited by 41
- 2021Memory-Augmented Deep Unfolding Network for Compressive Sensingcited by 38
- 2017MR image reconstruction using deep density priorscited by 37
- 2020Deep Low-rank plus Sparse Network for Dynamic MR Imagingcited by 32
- 2019VS-Net: Variable splitting network for accelerated parallel MRI reconstructioncited by 31
- 2019Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)cited by 30
- 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
- 2021One-dimensional Deep Low-rank and Sparse Network for Accelerated MRIcited by 27
- 2020Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space datacited by 24
- 2023Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstructioncited by 22
- 2018MRI Reconstruction via Cascaded Channel-wise Attention Networkcited by 20
- 2020Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurationscited by 18
- 2021Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularizationcited by 18
- 2023SMRD: SURE-based Robust MRI Reconstruction with Diffusion Modelscited by 16
- 2023Dynamic Path-Controllable Deep Unfolding Network for Compressive Sensingcited by 15
- 2019Pyramid Convolutional RNN for MRI Image Reconstructioncited by 14
- 2023Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstructioncited by 14
- 2019Image Reconstruction with Low-rankness and Self-consistency of k-space Data in Parallel MRIcited by 13
- 2019Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine Learningcited by 11
- 2022Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRIcited by 8
- 2023A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRIcited by 8
- 2023vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-Problemscited by 8
- 2023Fast Controllable Diffusion Models for Undersampled MRI Reconstructioncited by 8
- 2019Efficient Structurally-Strengthened Generative Adversarial Network for MRI Reconstructioncited by 5
- 2022T2LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstructioncited by 4
- 2023GA-HQS: MRI reconstruction via a generically accelerated unfolding approachcited by 3
- 2023CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstructioncited by 3
- 2023Improving DCE-MRI through unfolded low-rank + sparse optimisationcited by 1
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2 papers.