https://github.com/icon-lab/fdb
Official implementation of the Fourier-constrained diffusion bridges (FDB) model for MRI reconstruction
https://github.com/icon-lab/fdb
artificial-intelligence bridges deep-learning diffusion-models magnetic-resonance-imaging reconstruction score-based-generative-modeling
Last synced: 9 months ago
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Official implementation of the Fourier-constrained diffusion bridges (FDB) model for MRI reconstruction
- Host: GitHub
- URL: https://github.com/icon-lab/fdb
- Owner: icon-lab
- License: mit
- Created: 2023-08-04T14:01:57.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-20T11:56:24.000Z (almost 2 years ago)
- Last Synced: 2023-12-20T17:18:13.193Z (almost 2 years ago)
- Topics: artificial-intelligence, bridges, deep-learning, diffusion-models, magnetic-resonance-imaging, reconstruction, score-based-generative-modeling
- Language: Python
- Homepage:
- Size: 1.63 MB
- Stars: 16
- Watchers: 4
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# FDB
Official PyTorch implementation of FDB as described in the [paper](https://arxiv.org/abs/2308.01096)
Muhammad U. Mirza, Onat Dalmaz, Hasan A. Bedel, Gokberk Elmas, Yilmaz Korkmaz, Alper Gungor, Salman UH Dar, Tolga Çukur, "Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction", arXiv 2023.
## Dependencies
```
python==3.8.13
blobfile==2.0.2
h5py==3.9.0
imageio==2.22.1
mpi4py==3.1.4
numpy==1.24.4
Pillow==10.0.0
torch==2.0.1
```
## Installation
- Clone this repo:
```
git clone https://github.com/icon-lab/FDB
cd FDB
```
## Train
For Single-Coil
```
python train.py --data_dir /path_to_data/ --log_interval 5000 --save_dir 'model_singlecoil' --save_interval 5000 --image_size 256 --num_channels 128 --num_res_blocks 3 --learn_sigma False --dropout 0.3 --diffusion_steps 1000 --lr 1e-4 --batch_size 1 --lr_anneal_steps 100000 --undersampling_rate 2 --data_type 'singlecoil'
```
For Multi-Coil
```
python train.py --data_dir /path_to_data/ --log_interval 5000 --save_dir 'model_multicoil' --save_interval 5000 --image_size 384 --num_channels 128 --num_res_blocks 3 --learn_sigma False --dropout 0.3 --diffusion_steps 1000 --lr 1e-4 --batch_size 1 --lr_anneal_steps 15000 --undersampling_rate 2 --data_type 'multicoil'
```
## Inference
For Single-Coil
```
python sample.py --model_path model_singlecoil/ema_0.9999_100000.pt --data_path /path_to_data/ --image_size 256 --num_channels 128 --num_res_blocks 3 --learn_sigma False --dropout 0.3 --diffusion_steps 1500 --save_path results_singlecoil --num_samples 1 --batch_size 1 --data_type 'singlecoil' --R 4 --contrast 'T1'
```
For Multi-Coil
```
python sample.py --model_path model_multicoil/ema_0.9999_015000.pt --data_path /path_to_data/ --image_size 384 --num_channels 128 --num_res_blocks 3 --learn_sigma False --dropout 0.3 --diffusion_steps 1750 --save_path results_multicoil --num_samples 1 --batch_size 1 --data_type 'multicoil' --R 8 --contrast 'FLAIR'
```
# Citation
You are encouraged to modify/distribute this code. However, please acknowledge this code and cite the paper appropriately.
```
@misc{mirza2023learning,
title={Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction},
author={Muhammad U. Mirza and Onat Dalmaz and Hasan A. Bedel and Gokberk Elmas and Yilmaz Korkmaz and Alper Gungor and Salman UH Dar and Tolga Çukur},
year={2023},
eprint={2308.01096},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
For any questions, comments and contributions, please contact Usama Mirza (usama.mirza.819[at]gmail.com )
(c) ICON Lab 2023
# Acknowledgements
This code uses libraries from [DiffuseRecon](https://github.com/cpeng93/DiffuseRecon) and [Improved DDPM](https://github.com/openai/improved-diffusion) repositories.