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SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SEGAN instead of ReGAN | CS-MRI reconstruction · MICCAI 2013 T1 MRI, 10% sampling | SSIM: 0.83$\pm$0.02 -> 0.84$\pm$0.01 | Helped |
| Use SEGAN instead of ReGAN | CS-MRI reconstruction · MICCAI 2013 T1 MRI, 10% sampling | NMSE: 0.17$\pm$0.04 -> 0.17$\pm$0.04 | No clear effect |
| Use SEGAN instead of DAGAN | CS-MRI reconstruction · MICCAI 2013 T1 MRI, 20% sampling | SSIM: 0.95$\pm$0.03 -> 0.96$\pm$0.01 | Helped |
| Use SEGAN instead of ReGAN | CS-MRI reconstruction · MICCAI 2013 T1 MRI, 20% sampling | NMSE: 0.09$\pm$0.03 -> 0.08$\pm$0.01 | Helped |
| Use SEGAN instead of ReGAN | CS-MRI reconstruction · MICCAI 2013 T1 MRI, 30% sampling | SSIM: 0.96$\pm$0.02 -> 0.97$\pm$0.01 | Helped |
| Use SEGAN instead of ReGAN | CS-MRI reconstruction · MICCAI 2013 T1 MRI, 30% sampling | NMSE: 0.09$\pm$0.01 -> 0.07$\pm$0.01 | Helped |
| Use SEGAN instead of ReGAN | CS-MRI reconstruction · MICCAI 2013 T1 MRI, 40% sampling | NMSE: 0.06$\pm$0.02 -> 0.05$\pm$0.01 | Helped |
| Use SEGAN instead of ReGAN | CS-MRI reconstruction · MICCAI 2013 T1 MRI, 40% sampling | SSIM: 0.98$\pm$0.01 -> 0.98$\pm$0.01 | No clear effect |
Closest papers by meaning
- Deep Compressed Sensing (ICML 2019 · similarity 0.88)
- Compressed Sensing MRI Using a Recursive Dilated Network (AAAI 2018 · similarity 0.87)
- Task-Aware Compressed Sensing With Generative Adversarial Networks (AAAI 2018 · similarity 0.87)
- A Unified Model for Compressed Sensing MRI Across Undersampling Patterns (CVPR 2025 · similarity 0.86)
- pcaGAN: Improving Posterior-Sampling cGANs via Principal Component Regularization (NeurIPS 2024 · similarity 0.86)
- Improving Deep Learning for Accelerated MRI With Data Filtering (NeurIPS 2025 · similarity 0.84)
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