Awesome

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

This repo contains the JAX code for experiments in the paper Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

by Yang Song*, Liyue Shen*, Lei Xing, and Stefano Ermon. (*= joint first authors)

We propose a general approach to solving linear inverse problems in medical imaging with score-based generative models. Our method is purely generative, therefore does not require knowing the physical measurement process during training, and can be quickly adapted to different imaging processes at test time without model re-training. We have demonstrated superior performance on sparse-view computed tomography (CT), magnetic resonance imaging (MRI), and metal artifact removal (MAR) in CT imaging.

Dependencies

See requirements.txt.

Usage

Train and evaluate our models through main.py.

main.py:
  --config: Training configuration.
    (default: 'None')
  --eval_folder: The folder name for storing evaluation results
    (default: 'eval')
  --mode: <train|eval|tune>: Running mode: train or eval or tune
  --workdir: Working directory

• config is the path to the config file. Our prescribed config files are provided in configs/. They are formatted according to ml_collections and should be mostly self-explanatory. sampling.cs_solver specifies which sampling method we use for solving the inverse problems. They have 4 possible values:

  • baseline: The "Score SDE" approach, as in the original paper Score-Based Generative Modeling through Stochastic Differential Equations
  • langevin: The "Langevin" approach, similar to the method in Robust Compressed Sensing MRI with Deep Generative Priors
  • langevin_projection: The "ALD + Ours" approach, used to demonstrate advantages of the projection-based conditioning method compared to prior work.
  • projection: Our full method.

• workdir is the path that stores all artifacts of one experiment, like checkpoints, samples, and evaluation results.

• eval_folder is the name of a subfolder in workdir that stores all artifacts of the evaluation process, like meta checkpoints for pre-emption prevention, image samples, and numpy dumps of quantitative results.

• mode is "train", "eval", or "tune". When set to "train", it starts the training of a new model, or resumes the training of an old model if its meta-checkpoints (for resuming running after pre-emption in a cloud environment) exist in workdir/checkpoints-meta . When set to "eval", it computes the PSNR/SSIM metrics on a test dataset. When set to "tune", it automatically tunes hyperparameters for the sampler with Bayesian optimization.

Pretrained checkpoints

Checkpoints and test data are provided in this Google drive. Please download the folder and move it to the same directory of this repo.

References

If you find the code useful for your research, please consider citing

@inproceedings{
  song2022solving,
  title={Solving Inverse Problems in Medical Imaging with Score-Based Generative Models},
  author={Yang Song and Liyue Shen and Lei Xing and Stefano Ermon},
  booktitle={International Conference on Learning Representations},
  year={2022},
  url={https://openreview.net/forum?id=vaRCHVj0uGI}
}

and its prior work

@inproceedings{
  song2021scorebased,
  title={Score-Based Generative Modeling through Stochastic Differential Equations},
  author={Yang Song and Jascha Sohl-Dickstein and Diederik P Kingma and Abhishek Kumar and Stefano Ermon and Ben Poole},
  booktitle={International Conference on Learning Representations},
  year={2021},
  url={https://openreview.net/forum?id=PxTIG12RRHS}
}