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.
CITATION STYLE
Babu, A. V., Zhou, T., Kandel, S., Bicer, T., Liu, Z., Judge, W., … Cherukara, M. J. (2023). Deep learning at the edge enables real-time streaming ptychographic imaging. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-41496-z
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