LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring

This is the official implementation of the paper " LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring". The pre-print version can be found in arxiv; the early access version can be found in TMI.

Updates

Jan, 2024: initial commit.

Approach

Data Preparation

The 2016 AAPM-Mayo dataset can be downloaded from: CT Clinical Innovation Center (B30 kernel)
The 2020 AAPM-Mayo dataset can be downloaded from: cancer imaging archive

Dataset structre:

Mayo2016/
  |--train/
      |--quarter_lr/
        train_quarter_00001.npy
        train_quarter_00002.npy
        train_quarter_00003.npy
        ...
      |--full_hr/
        train_full_00001.npy
        train_full_00002.npy
        train_full_00003.npy
        ...
  |--test/
      |--quarter_1mm
      |--full_1mm

Requirements

- Linux Platform
- torch==1.12.1+cu113 # depends on the CUDA version of your machine
- torchvision==0.13.1+cu113
- Python==3.8.0
- numpy==1.22.3

Traning and & Inference

Training

python train.py --is_train --dataroot (path to dataset) --checkpoints_dir checkpoints(path to save model checkpoint)  --name litformer(name of project)  --train_batch_size 64 --phase train --is_val  --gpu_ids 0,1

Inference & testing

python test.py --is_test --dataroot (path to dataset)   --name litformer(name of project)   --model_path  (path to load model checkpoint) --phase test --test_batch_size 1  --gpu_ids 0

Please refer to options files for more setting.

Citation

If you find our work and code helpful, please kindly cite the corresponding paper:

@article{chen2024lit,
  title={LIT-Former: Linking in-plane and through-plane transformers for simultaneous CT image denoising and deblurring},
  author={Chen, Zhihao and Niu, Chuang and Gao, Qi and Wang, Ge and Shan, Hongming},
  journal={IEEE Transactions on Medical Imaging},
  year={2024},
  publisher={IEEE}
}