fdb's Introduction

FDB

Official implementation of the Fourier-constrained diffusion bridges (FDB) model for MRI reconstruction

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

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 --noise_schedule cosine --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 --noise_schedule cosine --lr 1e-4 --batch_size 1 --lr_anneal_steps 15000 --undersampling_rate 8 --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 --noise_schedule cosine --save_path results_singlecoil --num_samples 1 --batch_size 1 --data_type 'singlecoil' --R 5 --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 1000 --noise_schedule cosine --save_path results_multicoil --num_samples 1 --batch_size 1 --data_type 'multicoil' --R 8 --contrast 'FLAIR'

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