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MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add data consistency step (ablation) | RL pixel-wise MRI reconstruction · MICCAI 30% mask | PSNR: 38.90 -> 41.07 | Helped |
| add data consistency step (ablation) | RL pixel-wise MRI reconstruction · fastMRI 40% mask | PSNR: 29.7 -> 30.3 | Helped |
| learn continuous filter parameters instead of fixing them (ablation) | RL pixel-wise MRI reconstruction with DC · MICCAI 30% mask | PSNR: 40.12 -> 41.07 | Helped |
| learn continuous filter parameters instead of fixing them (no DC) (ablation) | RL pixel-wise MRI reconstruction · MICCAI 30% mask | PSNR: 37.39 -> 38.90 | Helped |
| RL method vs DAGAN | Ours with DC · MICCAI 10-50% masks | PSNR: 30.99 vs 33.79 (10%); 41.07 vs 40.20 (30%) | Mixed |
| RL method vs Unet | Ours with DC · fastMRI 40% mask | PSNR: 30.3 vs 30.4 | No clear effect |
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