DisC-Diff

DisC-Diff is multi-contrast brain MRI super-resolution method designed based on denoising diffusion probabilistic models. Specifically, DisC-Diff leverages a disentangled multi-stream network to exploit complementary information from multi-contrast MRI, improving model interpretation under multiple conditions of multi-contrast inputs.

Dataset & Pretrained Models

• The processed HCP dataset for training and testing.
• The models pretrained on HCP dataset under x2 & x4 resolution scale can be downloaded through this link.

Model Training

1. Modify the arguments hr_data_dir, lr_data_dir,and other_data_dir in config/config_train.yaml into the paths for your downloaded training T2-HR, T2-LR, and T1-HR data.
2. In train_job.sh, replace the second line into export PYTHONPATH= "Your Repository Path".
3. Run bash train_job.sh.

Model Evaluation

1. Modify the arguments hr_data_dir, lr_data_dir,and other_data_dir in config/config_test.yaml into the paths for your downloaded testing T2-HR, T2-LR, and T1-HR data.
2. In test_job.sh, replace the second line into export PYTHONPATH= "Your Repository Path".
3. Run bash test_job.sh.