Published October 28, 2023 | Version v1
Poster Open

LION v0.1: Learned Iterative Optimization Networks

Authors/Creators

  • 1. ROR icon University of Cambridge
  • 2. ROR icon University of Siegen

Description

Poster for v0.1 of LION, a toolbox of machine learning methods for tomographic image reconstruction. 

Files

Files (1.1 MB)

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Additional details

Dates

Available
2023

References

  • Hendriksen, Allard A., et al. "Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python." Optics Express 29.24 (2021): 40494-40513.
  • Adler, Jonas, and Ozan Öktem. "Learned primal-dual reconstruction." IEEE transactions on medical imaging 37.6 (2018): 1322-1332.
  • Genzel, Martin, Jan Macdonald, and Maximilian März. "AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry." arXiv preprint arXiv:2106.00280 (2021).
  • Jin, Kyong Hwan, et al. "Deep convolutional neural network for inverse problems in imaging." IEEE transactions on image processing 26.9 (2017): 4509-4522.
  • Lunz, Sebastian, Ozan Öktem, and Carola-Bibiane Schönlieb. "Adversarial regularizers in inverse problems." Advances in neural information processing systems 31 (2018).
  • Mukherjee, Subhadip, et al. "Learned convex regularizers for inverse problems." arXiv preprint arXiv:2008.02839 (2020).
  • Hendriksen, Allard Adriaan, Daniël Maria Pelt, and K. Joost Batenburg. "Noise2inverse: Self-supervised deep convolutional denoising for tomography." IEEE Transactions on Computational Imaging 6 (2020): 1320-1335.
  • Der Sarkissian, Henri, et al. "A cone-beam X-ray computed tomography data collection designed for machine learning." Scientific data 6.1 (2019): 215.