# Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning

arXiv: https://arxiv.org/abs/2001.02469 · HTML: https://mlsanity.com/paper/2001.02469/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery](https://mlsanity.com/paper/2108.13844/) (2021)
- [Deep Microlocal Reconstruction for Limited-Angle Tomography](https://mlsanity.com/paper/2108.05732/) (2021)
- [Physics-assisted Generative Adversarial Network for X-Ray Tomography](https://mlsanity.com/paper/2204.03703/) (2022)
- [Deep learning for inverse problems with unknown operator](https://mlsanity.com/paper/2108.02744/) (2021)
- [Advantage of Machine Learning over Maximum Likelihood in Limited-Angle Low-Photon X-Ray Tomography](https://mlsanity.com/paper/2111.08011/) (2021)
- [Data-Efficient Limited-Angle CT Using Deep Priors and Regularization](https://mlsanity.com/paper/2502.12293/) (2025)

## Abstract (arXiv)

In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision to other system parts or high attenuation at certain tilting angles. Image reconstruction from such limited angle data suffers from artifacts due to missing data. In this work, deep learning is applied to limited angle reconstruction in TXMs for the first time. With the challenge to obtain sufficient real data for training, training a deep neural network from synthetic data is investigated. Particularly, the U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images. The proposed method is evaluated on synthetic data and real scanned chlorella data in 100^circ limited angle tomography. For synthetic test data, the U-Net significantly reduces root-mean-square error (RMSE) from 2.55 × 10-3 μm-1 in the FBP reconstruction to 1.21 × 10-3 μm-1 in the U-Net reconstruction, and also improves structural similarity (SSIM) index from 0.625 to 0.920. With penalized weighted least square denoising of measured projections, the RMSE and SSIM are further improved to 1.16 × 10-3 μm-1 and 0.932, respectively. For real test data, the proposed method remarkably improves the 3-D visualization of the subcellular structures in the chlorella cell, which indicates its important value for nano-scale imaging in biology, nanoscience and materials science.