Deep learning at the edge enables real-time streaming ptychographic imaging
Abstract
Coherent imaging techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells. Driven by the construction of brighter sources and high-rate detectors, coherent imaging methods like ptychography are poised to revolutionize nanoscale materials characterization. However, these advancements are accompanied by significant increase in data and compute needs, which precludes real-time imaging, feedback and decision-making capabilities with conventional approaches. Here, we demonstrate a workflow that leverages artificial intelligence at the edge and high-performance computing to enable real-time inversion on X-ray ptychography data streamed directly from a detector at up to 2 kHz. The proposed AI-enabled workflow eliminates the oversampling constraints, allowing low-dose imaging using orders of magnitude less data than required by traditional methods.
- Publication:
-
Nature Communications
- Pub Date:
- November 2023
- DOI:
- arXiv:
- arXiv:2209.09408
- Bibcode:
- 2023NatCo..14.7059B
- Keywords:
-
- Computer Science - Machine Learning;
- Electrical Engineering and Systems Science - Image and Video Processing