# High-Resolution Limited-Angle Phase Tomography of Dense Layered Objects Using Deep Neural Networks

arXiv: https://arxiv.org/abs/1812.07380 · HTML: https://mlsanity.com/paper/1812.07380/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2018.

## Built on by (most cited first, 9 of 9)

- [Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data](https://mlsanity.com/paper/2309.06948/) (2023)
- [Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samples](https://mlsanity.com/paper/2103.15795/) (2021)
- [Physics-assisted Generative Adversarial Network for X-Ray Tomography](https://mlsanity.com/paper/2204.03703/) (2022)
- [LRIP-Net: Low-Resolution Image Prior based Network for Limited-Angle CT Reconstruction](https://mlsanity.com/paper/2208.00207/) (2022)
- [Deep-learning-driven Reliable Single-pixel Imaging with Uncertainty Approximation](https://mlsanity.com/paper/2107.11678/) (2021)
- [Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time](https://mlsanity.com/paper/2212.00014/) (2022)
- [Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine](https://mlsanity.com/paper/2503.19248/) (2025)
- [Joint Segmentation and Image Reconstruction with Error Prediction in Photoacoustic Imaging using Deep Learning](https://mlsanity.com/paper/2407.02653/) (2024)
- [Robustness of data-driven approaches in limited angle tomography](https://mlsanity.com/paper/2403.11350/) (2024)

## Abstract (arXiv)

We present a Machine Learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to ± 10^circ. Whereas previous approaches to phase tomography generally require two steps, first to retrieve phase projections from intensity projections and then perform tomographic reconstruction on the retrieved phase projections, in our work a physics-informed pre-processor followed by a Deep Neural Network (DNN) conduct the three-dimensional reconstruction directly from the intensity projections. We demonstrate this single-step method experimentally in the visible optical domain on a scaled up integrated circuit phantom. We show that even under conditions of highly attenuated photon fluxes a DNN trained only on synthetic data can be used to successfully reconstruct physical samples disjoint from the synthetic training set. Thus, the need of producing a large number of physical examples for training is ameliorated. The method is generally applicable to tomography with electromagnetic or other types of radiation at all bands.