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.
@article{arxiv.2512.16065,
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},
journal= {arXiv preprint arXiv:2512.16065},
year = {2026}
}