Single-View Tomographic Reconstruction Using Learned Primal Dual

Written by: Sean Breckling, Matthew Swan, Keith D. Tan, Derek Wingard, Brandon Baldonado, Yoohwan Kim, Ju-Yeon Jo, Evan Scott, and Jordan Pillow

Published on: Dec, 2025

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Summary

The Learned Primal Dual (LPD) method has shown promising results in various tomographic reconstruction modalities, particularly under challenging acquisition restrictions such as limited viewing angles or a limited number of views. We investigate the performance of LPD in a more extreme case: single-view tomographic reconstructions of axially-symmetric targets. This study considers two modalities: the first assumes low-divergence or parallel X-rays. The second models a cone-beam X-ray imaging testbed. For both modalities, training data is generated using closed-form integral transforms, or physics-based ray-tracing software, then corrupted with blur and noise. Our results are then compared against common numerical inversion methodologies.

Publication

Breckling, S., Swan, M., Tan, K. D., Wingard, D., Baldonado, B., Kim, Y., … Pillow, J. (2025). Single-view tomographic reconstruction using learned primal dual. arXiv. doi: 10.48550/arXiv.2512.16065 .

Citation

@article{breckling2025singleviewtomographicreconstructionusing,
  title={Single-View Tomographic Reconstruction Using Learned Primal Dual}, 
  author={Sean Breckling and Matthew Swan and Keith D. Tan and Derek Wingard and 
    Brandon Baldonado and Yoohwan Kim and Ju-Yeon Jo and Evan Scott and Jordan Pillow},
  year={2025},
  journal={arXiv},
  doi={10.48550/arXiv.2512.16065}, 
}