KI-EBM

Paper: K-space and image domain collaborative energy-based model for parallel MRI reconstruction https://www.sciencedirect.com/science/article/abs/pii/S0730725X23000383

Authors: Zongjiang Tu, Chen Jiang, Yu Guan, Jijun Liu, Qiegen Liu

Date: March. 21, 2023
Version: 2.0
The code and the algorithm are for non-comercial use only.
Copyright 2022, Department of Electronic Information Engineering, Nanchang University.

Pretrained Checkpoints

We provide pretrained checkpoints. You can download pretrained models from [Google Drive]

https://drive.google.com/drive/folders/1GV1L4d_DDDgNfzGojhTefZbft_uK7Yda?usp=sharing

Test

If you want to test the code，please

# Compare with MoDL random-2D R6

pKIEBM_Test_Demo.ipynb

sKIEBM_Test_Demo.ipynb

CUDA_VISIBLE_DEVICES=0 python3 PKI_compare_modl.py --swish_act --exp_I=SIAT_I --resume_iter_I=169500 --exp_K=SIAT_K --resume_iter_K=124500 --step_lr_I=10 --step_lr_K=10

CUDA_VISIBLE_DEVICES=0 python3 SKI_compare_modl.py --swish_act --exp_I=SIAT_I --resume_iter_I=169500 --exp_K=SIAT_K --resume_iter_K=124500 --step_lr_I=10 --step_lr_K=10

# 8ch random-2D GRBrain R4
CUDA_VISIBLE_DEVICES=0 python3 Test_PKI_8ch_demo.py --swish_act --exp_I=SIAT_I --resume_iter_I=169500 --exp_K=SIAT_K --resume_iter_K=124500 --step_lr_I=300 --step_lr_K=100

Acknowledgement

The implementation is based on

https://github.com/openai/ebm_code_release

https://github.com/yqx7150/EBMRec