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Recurrent Variational Network: A Deep Learning Inverse Problem Solver Applied to the Task of Accelerated MRI Reconstruction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use RecurrentVarNet instead of RIM | MRI reconstruction · Calgary-Campinas, R=5 | SSIM: 0.9381 -> 0.9418 | Helped |
| use RecurrentVarNet instead of RIM | MRI reconstruction · Calgary-Campinas, R=10 | pSNR: 32.52 -> 32.71 | Helped |
| add the SER sensitivity-map refinement module (ablation) | RecurrentVarNet · Calgary-Campinas, R=5 | SSIM: 0.9374 -> 0.9418 | Helped |
| add the SER sensitivity-map refinement module (ablation) | RecurrentVarNet · Calgary-Campinas, R=10 | SSIM: 0.8960 -> 0.9073 | Helped |
| use separate recurrent-unit weights for each block instead of shared weights (ablation) | RecurrentVarNet · Calgary-Campinas, R=5 | SSIM: 0.9386 -> 0.9418 | Helped |
| use separate recurrent-unit weights for each block instead of shared weights (ablation) | RecurrentVarNet · Calgary-Campinas, R=10 | SSIM: 0.9017 -> 0.9073 | Helped |
| add the learned recurrent state initializer rather than zero-initializing hidden state (ablation) | RecurrentVarNet · Calgary-Campinas, R=5 | SSIM: 0.9411 -> 0.9418 | Helped |
| add the learned recurrent state initializer rather than zero-initializing hidden state (ablation) | RecurrentVarNet · Calgary-Campinas, R=10 | SSIM: 0.9071 -> 0.9073 | Helped |
| increase unrolled steps to T=11 and reduce recurrent layers to n_l=3 (ablation) | RecurrentVarNet · Calgary-Campinas, R=5 | SSIM: 0.9418 -> 0.9417 | No clear effect |
| increase unrolled steps to T=11 and reduce recurrent layers to n_l=3 (ablation) | RecurrentVarNet · Calgary-Campinas, R=10 | SSIM: 0.9073 -> 0.9093 | Helped |
| use the reported reduced-parameter ablation configuration (ablation) | RecurrentVarNet · fastMRI AXT1, R=4 | SSIM: 0.960 -> 0.959 | Hurt |
| use the reported reduced-parameter ablation configuration (ablation) | RecurrentVarNet · fastMRI AXT1, R=8 | SSIM: 0.941 -> 0.938 | Hurt |
Closest papers by meaning
- A Unified Model for Compressed Sensing MRI Across Undersampling Patterns (CVPR 2025 · similarity 0.89)
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- Improving Deep Learning for Accelerated MRI With Data Filtering (NeurIPS 2025 · similarity 0.88)
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