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Related Experiment Video

Updated: Jun 18, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

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

Anakha V Babu^1,2, Tao Zhou¹, Saugat Kandel¹

¹Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL, USA.

Nature Communications

|November 4, 2023

Summary

This study introduces an AI-powered workflow for real-time X-ray ptychography (XRP) imaging. This approach significantly reduces data requirements and enables low-dose nanoscale materials characterization.

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Area of Science:

Materials Science
Imaging Techniques
Artificial Intelligence

Background:

Coherent imaging offers multi-scale material insights.
Advancements in sources and detectors drive techniques like ptychography for nanoscale characterization.
High data and compute demands hinder real-time capabilities.

Purpose of the Study:

To develop a real-time inversion workflow for X-ray ptychography (XRP) data.
To overcome limitations of conventional methods in data processing and imaging speed.
To enable low-dose imaging with reduced data requirements.

Main Methods:

Leveraging artificial intelligence (AI) at the edge for data processing.
Utilizing high-performance computing for accelerated analysis.
Streaming X-ray ptychography data directly from detectors at high rates (up to 2 kHz).

Main Results:

Demonstrated a real-time AI-enabled workflow for XRP data inversion.
Achieved real-time processing at rates up to 2 kHz.
Eliminated oversampling constraints, enabling low-dose imaging with significantly less data.

Conclusions:

The AI-enabled workflow revolutionizes nanoscale materials characterization.
Real-time XRP imaging is now feasible, facilitating advanced feedback and decision-making.
This approach significantly reduces data and computational burdens for coherent imaging.