Polyner

This repository is the official implementation of our NeurIPS 2023 paper "Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction" [OpenReview], [arXiv]

Fig. 1: Overview of the proposed Polyner model.

1. Visualization

Fig. 2: Qualitative results of FBP and our polyner on 2D fan-beam samples of DeepLesion simulation dataset.

Fig. 3: Qualitative results of FDK and our polyner on a real-world 3D cone-beam mouse thigh sample.

2. File Tree

Polyner
│  config.json                  # configuration script.
│  dataset.py                   # dataloader
│  eval.py                  # quantitative evaluation
│  main.py                  # running script for training
│  model.py                 # EAS loss
│  readme.md                    # readme file
│  Polyner.py                   # training function
│  utils.py                 # tools
│  
├─data_simulation               # data simulation
│  │  config_dl.yaml                # acquisition parameters
│  │  dl_data.m                 # running script for DeepLesion dataset
│  │  
│  ├─+helper                    # functions for data simulation
│  │      get_mar_params.m
│  │      interpolate_projection.m
│  │      pkev2kvp.m
│  │      simulate_metal_artifact.m
│  │              
│  ├─metal                  # prior data for simulation
│  │      
│  └─slice
│          gt_x.nii             # raw data
│      
├─input
│      fanSensorPos.nii             # geometry angle
│      GE14Spectrum120KVP.mat           # energy spectrum
│      gt_x.nii                 # gt image
│      mask_x.nii               # metal mask
│      ma_x.nii                 # FBP reconstructions
│      ma_sinogram_x.nii            # metal-corrupted measurements
│      
├─model
│      model_x.pkl              # pre-trained Polyner
│      
└─output
        polyner_x.nii               # Polyner reconstructions

3. Main Requirements

To run this project, you will need the following packages:

• PyTorch 3.8.13
• tinycudann
• SimpleITK, tqdm, numpy, and other packages.

4. Training and Checkpoints

To train our Polyner from scratch, navigate to ./ and run the following command in your terminal:

python main.py

This will train the Polyner model for the metal-corrputed sinogram (./input/ma_sinogram_0~9.nii). The well-trained model will be stored in ./model and its corresponding MAR results will be stored in ./output.

5. Evaluation

To qualitatively evalute the result, navigate to ./ and run the following comman in your terminal:

python eval.py

This will compute PSNR and SSIM values of FBP and our Polyner on the ten samples of the DeepLesion dataset.

For the ten sinograms (./input/ma_sinogram_0~9.nii), the quantitative results are shown in:

6. Data Simulation

To simulate the metal-corrupted measurements, navigate to ./data_simulation and run the MATLAB script dl_data.m. These code for data simulation are based on the ADN repository: https://github.com/liaohaofu/adn/tree/master

7. Others

NIFTI files (.nii) can be viewed by using the ITK-SNAP software, which is available for free download at: http://www.itksnap.org/pmwiki/pmwiki.php?n=Downloads.SNAP4

8. License

This code is available for non-commercial research and education purposes only. It is not allowed to be reproduced, exchanged, sold, or used for profit.

9. Citation

If you find our work useful in your research, please cite:

@inproceedings{
wu2023unsupervised,
title={Unsupervised Polychromatic Neural Representation for {CT} Metal Artifact Reduction},
author={Qing Wu and Lixuan Chen and Ce Wang and Hongjiang Wei and S Kevin Zhou and Jingyi Yu and Yuyao Zhang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=xx3QgKyghS}
}