TC-DiffRecon

This codebase is modified based on Improved DDPM

Overall structure of the TC-DiffRecon:

C2F Sampling Process:

Model renderings:

1. Installation

Clone this repository and navigate to it in your terminal. Then run:

pip install -e .

This should install the improved_diffusion python package that the scripts depend on.

2. Data Preparation

For fastMRI, the simplified h5 data can be downloaded by following the instructions in ReconFormer, i.e. through Link, which is the preprocessed fastMRI data. And the passport is: pguo4. DiffuseRecon converts it to a normalized format in scripts/data_process.py:

python scripts/data_process.py

3. Sampling

python scripts/image_sample_complex_duo.py --model_path img_space_dual/ema_0.9999_150000.pt --data_path EVAL_PATH \
--image_size 320 --num_channels 128 --num_res_blocks 3 --learn_sigma False --dropout 0.3 --diffusion_steps 4000 \
--noise_schedule cosine --timestep_respacing 100 --save_path test/ --num_samples 1 --batch_size 5

Note that timestep_respacing indicates the initial coarse sampling steps.

4. Training

mpiexec -n GPU_NUMS python scripts/image_train.py --data_dir TRAIN_PATH --image_size 320 --num_channels 128\
 --num_res_blocks 3 --learn_sigma False --dropout 0.3 --diffusion_steps 4000 --noise_schedule cosine --lr 1e-4 --batch_size 1\
--save_dir img_space_dual

5. Citation

@article{zhang2024tc,
  title={TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method},
  author={Zhang, Chenyan and Chen, Yifei and Fan, Zhenxiong and Huang, Yiyu and Weng, Wenchao and Ge, Ruiquan and Zeng, Dong and Wang, Changmiao},
  journal={arXiv preprint arXiv:2402.11274},
  year={2024}
}