🔥🔥Res-SRDiff is a deep learning framework designed to robustly restore high-resolution pelvic T2w MRI and ultra-high field brain T1 maps using an efficient probabilistic diffusion model.

• Our paper on Physics in Medicine and Biology: MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting ❤️

The following diagram illustrates the diffusion process used in this project:

• Python (>=3.12)
• PyTorch (>=2.5)
• NVIDIA CUDA (for GPU acceleration)
• Additional dependencies as listed in requirements.txt

1. Clone the repository:

   git clone https://github.com/mosaf/Res-SRDiff.git
   cd Res-SRDiff
   

2. Install dependencies:

   conda env update --file environment.yml --prune
   

You can download the pretrained model weights below:

• 🔗 Download Res-SRDiff weights for Prostate (Google Drive)

After downloading, place the .pt files into a folder named out_dir:

mkdir -p out_dir
mv path_to_downloaded_weights.pt out_dir/

⚠️ Note: The provided weights are for research use only and were trained on public datasets.

To run the project, modify the parameters in the main.py file and execute the main.py script:

python main.py

The diagram below visualizes the key hyper-parameters used in this model:

If you find Res-SRDiff useful for your research or project, please consider citing our work:

@article{10.1088/1361-6560/ade049,
	author={Safari, Mojtaba and Wang, Shansong and Eidex, Zach and Li, Qiang and Qiu, Richard L J and Middlebrooks, Erik H and Yu, David S and Yang, Xiaofeng},
	title={MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting},
	journal={Physics in Medicine \& Biology},
	url={https://iopscience.iop.org/article/10.1088/1361-6560/ade049},
	doi={https://doi.org/10.1088/1361-6560/ade049},
	year={2025}
}

• This project is based on ResShift ❤️.