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Deep learning at the edge enables real-time streaming ptychographic imaging

Physics

Deep learning at the edge enables real-time streaming ptychographic imaging

A. V. Babu, T. Zhou, et al.

Discover how a team of researchers, including Anakha V. Babu and Tao Zhou, have leveraged artificial intelligence combined with high-performance computing to perform real-time inversion on X-ray ptychography data at unprecedented speeds. This groundbreaking approach allows for low-dose imaging with significantly less data, transforming high-resolution imaging techniques into real-time capabilities.

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Playback language: English
Abstract
This paper demonstrates a workflow using artificial intelligence (AI) 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 AI-enabled workflow eliminates oversampling constraints, allowing low-dose imaging with significantly less data than traditional methods. X-ray ptychography, a high-resolution imaging technique, is enhanced by this approach to achieve real-time imaging capabilities.
Publisher
Nature Communications
Published On
Nov 03, 2023
Authors
Anakha V. Babu, Tao Zhou, Saugat Kandel, Tekin Bicer, Zhengchun Liu, William Judge, Daniel J. Ching, Yi Jiang, Sinisa Veseli, Steven Henke, Ryan Chard, Yudong Yao, Ekaterina Sirazitdinova, Geetika Gupta, Martin V. Holt, Ian T. Foster, Antonino Miceli, Mathew J. Cherukara
Tags
artificial intelligence
X-ray ptychography
real-time imaging
low-dose imaging
high-performance computing
data streaming

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