基于傅里叶约束弥散FDB模型实现MRI重构内含数据集和环境说明.zip

# 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. <img src="./figures/ddpm_vs_fdb.png" width="600px"> ## 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 <br /> 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' ``` <br /> ## Inference <br /> 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' ```