mlsanityData to 14 Oct 2026

A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction

arXiv 2207.01791 ↗First posted 5 Jul 2022eess.IV cs.CVcited by 6

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Abstract (arXiv)

Deep learning networks have shown promising results in fast magnetic resonance imaging (MRI) reconstruction. In our work, we develop deep networks to further improve the quantitative and the perceptual quality of reconstruction. To begin with, we propose reconsynergynet (RSN), a network that combines the complementary benefits of independently operating on both the image and the Fourier domain. For a single-coil acquisition, we introduce deep cascade RSN (DC-RSN), a cascade of RSN blocks interleaved with data fidelity (DF) units. Secondly, we improve the structure recovery of DC-RSN for T2 weighted Imaging (T2WI) through assistance of T1 weighted imaging (T1WI), a sequence with short acquisition time. T1 assistance is provided to DC-RSN through a gradient of log feature (GOLF) fusion. Furthermore, we propose perceptual refinement network (PRN) to refine the reconstructions for better visual information fidelity (VIF), a metric highly correlated to radiologists opinion on the image quality. Lastly, for multi-coil acquisition, we propose variable splitting RSN (VS-RSN), a deep cascade of blocks, each block containing RSN, multi-coil DF unit, and a weighted average module. We extensively validate our models DC-RSN and VS-RSN for single-coil and multi-coil acquisitions and report the state-of-the-art performance. We obtain a SSIM of 0.768, 0.923, 0.878 for knee single-coil-4x, multi-coil-4x, and multi-coil-8x in fastMRI. We also conduct experiments to demonstrate the efficacy of GOLF based T1 assistance and PRN.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 18 of its 45 references that are in the map. First posted on arXiv 5 Jul 2022.

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18 of 45 references.

References · 18

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
  4. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
  5. 2017The Perception-Distortion Tradeoffcited by 587
  6. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
  7. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 387
  8. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 342
  9. 2017Image reconstruction by domain transform manifold learningcited by 232
  10. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 202
  11. 2017Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Losscited by 82
  12. 2017A Deep Cascade of Convolutional Neural Networks for MR Image Reconstructioncited by 74
6 more
  1. 2018HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentationcited by 66
  2. 2019VS-Net: Variable splitting network for accelerated parallel MRI reconstructioncited by 31
  3. 2018A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstructioncited by 23
  4. 2018Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Networkcited by 21
  5. 2016Gland Instance Segmentation Using Deep Multichannel Neural Networkscited by 11
  6. 2019dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performancecited by 5

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Cited by year

Citing papers per year: 2023: 4; 2024: 0; 2025: 2.

6 papers.

  1. 2023Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problemscited by 5
  2. 2023Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworkscited by 2
  3. 2023HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstructioncited by 2
  4. 2023Partition-based K-space Synthesis for Multi-contrast Parallel Imagingcited by 1
  5. 2025A Comprehensive Survey on Magnetic Resonance Image Reconstructioncited by 1
  6. 2025Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integrationcited by 0