InteractiveSmoothingDBT

The aim of the work is to provide control over image quality for volumes reconstructed with PWLS algorithm. It introduces modifications in U-Net which allows it to filter the input for a given beta value.

Jupyter Notebook for the MICCAI 2021 paper: Interactive Smoothing Parameter Optimizationin DBT Reconstruction using Deep learning

Sample images and beta values are uploaded as ground_sample.zip and value_sample.zip.

References

Public Walnut CT dataset Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", Sci Data 6, 215 (2019) or arXiv:1905.04787 (2019)

Contributors

Pranjal Sahu ([email protected])

Citation

@inproceedings{sahu2021interactive,
  title={Interactive Smoothing Parameter Optimization in DBT Reconstruction Using Deep Learning},
  author={Sahu, Pranjal and Huang, Hailiang and Zhao, Wei and Qin, Hong},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={57--67},
  year={2021},
  organization={Springer}
}