ArticleNature communications2023
Deep learning at the edge enables real-time streaming ptychographic imaging.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- IPSBrain: A Unified Intelligent Data Analysis Platform for Multimodal Experimental Characterization at Advanced Photon Source.The journal of physical chemistry. A · 2026Article
- Operando microscopy for neuromorphic hardware.Nature materials · 2026Review
- In situ ptychographic x-ray nanotomography of temperature-controlled crystallization processes.Nature communications · 2026Article
- A highly energy-efficient multi-core neuromorphic architecture for training deep spiking neural networks.Nature communications · 2026Article
- phaser: a unified and extensible framework for fast electron ptychography.npj computational materials · 2026Article
- Fourier ptychographic enhancement of iterative pathways: autonomous 3D momentum coordination in hybrid ML-PIE architectures.Biomedical optics express · 2025Article
- Deep learning of structural morphology imaged by scanning X-ray diffraction microscopy.Scientific reports · 2025Article
- Near-Sensor Edge Computing System Enabled by a CMOS Compatible Photonic Integrated Circuit Platform Using Bilayer AlN/Si Waveguides.Nano-micro letters · 2025Article
- PvaPy streaming framework for real-time data processing.Journal of synchrotron radiation · 2025Article
- Ptychography at all wavelengths.Nature reviews. Methods primers · 2025Article
- Review
- Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.