基于深度扩散模型的自监督弥散 MRI 去噪内含数据集和环境搭建教程.zip

# DDM²: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models, ICLR 2023 Paper: https://arxiv.org/pdf/2302.03018.pdf   ## Dependencies Please clone our environment using the following command: ``` conda env create -f environment.yml conda activate ddm2 ``` ## Usage ### Data For fair evaluations, we used the data provided in the [DIPY](https://dipy.org/) library. One can easily access their provided data (e.g. Sherbrooke and Stanford HARDI) by using their official loading script: ```python3 hardi_fname, hardi_bval_fname, hardi_bvec_fname = get_fnames('stanford_hardi') data, affine = load_nifti(hardi_fname) ``` ### Configs Different experiments are controlled by configuration files, which are in ```config/```. We have provided default training configurations for reproducing our experiments. Users are required to **change the path vairables** to their own directory/data before running any experiments. *More detailed guidances are provided as inline comments in the config files.* ### Train The training of DDM² contains three sequential stages. For each stage, a corresponding config file (or an update of the original config file) need to be passed as a coommand line arg. 1. To train our Stage I: ```python3 train_noise_model.py -p train -c config/hardi_150.json``` or alternatively, modify ```run_stage1.sh``` and run: ```./run_stage1.sh``` 2. After Stage I training completed, the path to the checkpoint of the noise model need to be specific at 'resume_state' of the 'noise_model' section in corresponding config file. Additionally, a file path (.txt) needs to be specified at 'initial_stage_file' in the 'noise_model' section. This file will be recorded with the matched states in Stage II. 3. To process our Stage II: ```python3 match_state.py -p train -c config/hardi_150.json``` or alternatively, modify ```run_stage2.sh``` and run: ```./run_stage2.sh``` 4. After Stage II finished, the state file (a '.txt' file, generated in the previous step) needs to be specified at **'stage2_file'** variable in the last line of each config file. This step is neccesary for the following steps and inference. 5. To train our Stage III: ```python3 train_diff_model.py -p train -c config/hardi_150.json``` or alternatively, modify ```run_stage3.sh``` and run: ```./run_stage3.sh``` 6. Validation results along with checkpoints will be saved in the ```/experiments``` folder. ### Inference (Denoise) One can use the previously trained Stage III model to denoise a MRI dataset through: ```python denoise.py -c config/hardi.json``` or alternatively, modify ```denoise.sh``` and run: ```./denoise.sh``` The ```--save``` flag can be used to save the denoised reusults into a single '.nii.gz' file: ```python denoise.py -c config/hardi.json --save``` ### Quantitative Metrics Calulation With the denoised Stanford HARDI dataset, please follow the instructions in ```quantitative_metrics.ipynb``` to calculate SNR and CNR scores. *This notebook is derived from this [DIPY script](https://dipy.org/documentation/1.1.0./examples_built/snr_in_cc/). Please respect their license of usage.* ## Citation If you find this repo useful in your work or research, please cite: ``` @inproceedings{xiangddm, title={DDM $\^{} 2$: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models}, author={Xiang, Tiange and Yurt, Mahmut and Syed, Ali B and Setsompop, Kawin and Chaudhari, Akshay}, booktitle={The Eleventh International Conference on Learning Representations} } ```