# Adorym: A multi-platform generic x-ray image reconstruction framework based on automatic differentiation

arXiv: https://arxiv.org/abs/2012.12686 · HTML: https://mlsanity.com/paper/2012.12686/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [A parameter refinement method for Ptychography based on Deep Learning concepts](https://mlsanity.com/paper/2105.08058/) (2021)
- [Predicting ptychography probe positions using single-shot phase retrieval neural network](https://mlsanity.com/paper/2405.20910/) (2024)
- [Differentiable Uncalibrated Imaging](https://mlsanity.com/paper/2211.10525/) (2022)
- [DONUT: Physics-aware Machine Learning for Real-time X-ray Nanodiffraction Analysis](https://mlsanity.com/paper/2507.14038/) (2025)
- [Learning neural representations for X-ray ptychography reconstruction with unknown probes](https://mlsanity.com/paper/2509.04402/) (2025)
- [A unified self-supervised framework for single-frame Fresnel CDI and overlapped ptychography](https://mlsanity.com/paper/2602.21361/) (2026)
- [On the conditional equivalence of phase retrieval algorithms](https://mlsanity.com/paper/2606.07257/) (2026)

## Abstract (arXiv)

We describe and demonstrate an optimization-based x-ray image reconstruction framework called Adorym. Our framework provides a generic forward model, allowing one code framework to be used for a wide range of imaging methods ranging from near-field holography to and fly-scan ptychographic tomography. By using automatic differentiation for optimization, Adorym has the flexibility to refine experimental parameters including probe positions, multiple hologram alignment, and object tilts. It is written with strong support for parallel processing, allowing large datasets to be processed on high-performance computing systems. We demonstrate its use on several experimental datasets to show improved image quality through parameter refinement.