# AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources

arXiv: https://arxiv.org/abs/2304.04297 · HTML: https://mlsanity.com/paper/2304.04297/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 68 references that are in the map.
- First posted on arXiv 9 Apr 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Learning Techniques for Inverse Problems in Imaging](https://mlsanity.com/paper/2005.06001/) (2020)
- [funcX: A Federated Function Serving Fabric for Science](https://mlsanity.com/paper/2005.04215/) (2020)
- [TomoGAN: Low-Dose Synchrotron X-Ray Tomography with Generative Adversarial Networks](https://mlsanity.com/paper/1902.07582/) (2019)
- [AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Bragg Coherent Diffraction Imaging](https://mlsanity.com/paper/2109.14053/) (2021)
- [Real-time coherent diffraction inversion using deep generative networks](https://mlsanity.com/paper/1806.03992/) (2018)
- [AI Accelerator Survey and Trends](https://mlsanity.com/paper/2109.08957/) (2021)
- [Deep learning at the edge enables real-time streaming ptychographic imaging](https://mlsanity.com/paper/2209.09408/) (2022)
- [BraggNN: Fast X-ray Bragg Peak Analysis Using Deep Learning](https://mlsanity.com/paper/2008.08198/) (2020)
- [Joint ptycho-tomography with deep generative priors](https://mlsanity.com/paper/2009.09498/) (2020)
- [SHARP: a distributed, GPU-based ptychographic solver](https://mlsanity.com/paper/1602.01448/) (2016)
- [Deep Learning Accelerated Light Source Experiments](https://mlsanity.com/paper/1910.04081/) (2019)
- [Algorithm-driven Advances for Scientific CT Instruments: From Model-based to Deep Learning-based Approaches](https://mlsanity.com/paper/2104.08228/) (2021)
- [Scalable and accurate multi-GPU based image reconstruction of large-scale ptychography data](https://mlsanity.com/paper/2106.07575/) (2021)
- [Petascale XCT: 3D Image Reconstruction with Hierarchical Communications on Multi-GPU Nodes](https://mlsanity.com/paper/2009.07226/) (2020)
- [Globus Automation Services: Research process automation across the space-time continuum](https://mlsanity.com/paper/2208.09513/) (2022)
- [iFDK: A Scalable Framework for Instant High-resolution Image Reconstruction](https://mlsanity.com/paper/1909.02724/) (2019)
- [Deep Learning-based Low-dose Tomography Reconstruction with Hybrid-dose Measurements](https://mlsanity.com/paper/2009.13589/) (2020)
- [High-Performance Ptychographic Reconstruction with Federated Facilities](https://mlsanity.com/paper/2111.11330/) (2021)
- [Image Gradient Decomposition for Parallel and Memory-Efficient Ptychographic Reconstruction](https://mlsanity.com/paper/2205.06327/) (2022)

## Built on by (most cited first, 1 of 1)

- [Ptychographic Image Reconstruction from Limited Data via Score-Based Diffusion Models with Physics-Guidance](https://mlsanity.com/paper/2502.18767/) (2025)

## Abstract (arXiv)

We present an end-to-end automated workflow that uses large-scale remote compute resources and an embedded GPU platform at the edge to enable AI/ML-accelerated real-time analysis of data collected for x-ray ptychography. Ptychography is a lensless method that is being used to image samples through a simultaneous numerical inversion of a large number of diffraction patterns from adjacent overlapping scan positions. This acquisition method can enable nanoscale imaging with x-rays and electrons, but this often requires very large experimental datasets and commensurately high turnaround times, which can limit experimental capabilities such as real-time experimental steering and low-latency monitoring. In this work, we introduce a software system that can automate ptychography data analysis tasks. We accelerate the data analysis pipeline by using a modified version of PtychoNN -- an ML-based approach to solve phase retrieval problem that shows two orders of magnitude speedup compared to traditional iterative methods. Further, our system coordinates and overlaps different data analysis tasks to minimize synchronization overhead between different stages of the workflow. We evaluate our workflow system with real-world experimental workloads from the 26ID beamline at Advanced Photon Source and ThetaGPU cluster at Argonne Leadership Computing Resources.