MRI Super-Resolution via Elucidated Diffusion Models
Pretrained model weights for the paper:
Comparative Analysis of 3D Convolutional and 2.5D Slice-Conditioned U-Net Architectures for MRI Super-Resolution via Elucidated Diffusion Models
Authors: Hendrik Chiche, Ludovic Corcos, Logan Rouge (in collaboration with GENCI)
Models
2.5D EDM Upsampler (agent_epoch_00010.pt)
- Architecture: 2D U-Net with slice conditioning, channels [64, 64, 128, 256]
- Parameters: 51.1M
- Training: 10 epochs on 59 NKI subjects (FOMO60K), AdamW lr=1e-4
- PSNR: 33.68 dB | SSIM: 0.967 (on held-out NKI test set, 2x SR)
- Inference: single-step Heun sampler (0.09s/slice on MPS)
3D EDM Upsampler (model_final.pt)
- Architecture: 3D U-Net with volumetric convolutions, channels [32, 64, 128, 256]
- Parameters: 50.7M
- Training: 10 epochs on 59 NKI subjects (FOMO60K), AdamW lr=1e-4
- PSNR: 37.77 dB | SSIM: 0.996 (on held-out NKI test set, 2x SR)
- Inference: 20-step Euler sampler with patch-based processing
Dataset
- 59 training subjects, 5 test subjects
- T1-weighted sagittal brain MRI
- 2x downsampling via block averaging (LR: 128x128, HR: 256x256)
Framework
Both models use the Elucidated Diffusion Model (EDM) framework from Karras et al. 2022,
adapted from the DIAMOND game world modeling codebase.
Usage
