mlsanityData to 13 Oct 2026

GA-HQS: MRI reconstruction via a generically accelerated unfolding approach

arXiv 2304.02883 ↗First posted 6 Apr 2023eess.IV cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Deep unfolding networks (DUNs) are the foremost methods in the realm of compressed sensing MRI, as they can employ learnable networks to facilitate interpretable forward-inference operators. However, several daunting issues still exist, including the heavy dependency on the first-order optimization algorithms, the insufficient information fusion mechanisms, and the limitation of capturing long-range relationships. To address the issues, we propose a Generically Accelerated Half-Quadratic Splitting (GA-HQS) algorithm that incorporates second-order gradient information and pyramid attention modules for the delicate fusion of inputs at the pixel level. Moreover, a multi-scale split transformer is also designed to enhance the global feature representation. Comprehensive experiments demonstrate that our method surpasses previous ones on single-coil MRI acceleration tasks.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 13 of its 28 references that are in the map. First posted on arXiv 6 Apr 2023.

Builds on

13 of 28 references.

References · 13

  1. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 467
  2. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 386
  3. 2015A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insightscited by 205
  4. 2018Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networkscited by 120
  5. 2018Denoising Prior Driven Deep Neural Network for Image Restorationcited by 80
  6. 2022Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imagingcited by 66
  7. 2018Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networkscited by 53
  8. 2021ISTA-Net++: Flexible Deep Unfolding Network for Compressive Sensingcited by 39
  9. 2018MRI Reconstruction via Cascaded Channel-wise Attention Networkcited by 20
  10. 2018ADMM and Accelerated ADMM as Continuous Dynamical Systemscited by 6
  11. 2021Learned Half-Quadratic Splitting Network for MR Image Reconstructioncited by 5
  12. 2022Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problemscited by 3
1 more
  1. 2022Generalized Deep Learning-based Proximal Gradient Descent for MR Reconstructioncited by 1

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Score-based Generative Priors Guided Model-driven Network for MRI Reconstructioncited by 3
  2. 2024MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstructioncited by 2
  3. 2025Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstructioncited by 1