TC-KANRecon

Overall structure of the TC-KANRecon:

model

Dynamic Clipping Strategy Process:

Strategy

Model Generate Detailed Effect Comparison(AF=4):

renderings

1. Installation

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

pip install -r requirements .

2. Data Preparation

The two datasets we used are both public datasets. For firstMRI, you can find it in Link, which includes 1172 subjects with more than 41,020 slice data; for SKM-TEA, you can find it in Link, which includes 155 subjects with more than 24,800 slice data. Both of them use the single-coil data of their knee.

When you have your data set ready, you need to change your data set path in the configuration file below:

for vae:

python config/vae/config_monaivae_zheer.py

for model:

python config/diffusion/config_controlnet.py

3. Training

for vae:

python my_vqvae/train_vae.py

for model:

python stable_diffusion/train_sd.py
python stable_diffusion/trian_model.py

4. Evaluating

python stable_diffusion/val_model.py

5. Citation

@article{ge2024tc,
  title={TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scaling},
  author={Ge, Ruiquan and Yu, Xiao and Chen, Yifei and Jia, Fan and Zhu, Shenghao and Zhou, Guanyu and Huang, Yiyu and Zhang, Chenyan and Zeng, Dong and Wang, Changmiao and others},
  journal={arXiv preprint arXiv:2408.05705},
  year={2024}
}