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Toward Real-Time Analysis of Synchrotron Micro-Tomography Data: Accelerating Experimental Workflows with AI and HPC

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Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI (SMC 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1315))

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Abstract

Synchrotron light sources are routinely used to perform imaging experiments. In this paper, we review the relevant computational stages, identify bottlenecks, and highlight future opportunities to streamline data acquisition for experimental microscopy workflows. We demonstrate our preliminary exploration with an end-to-end scientific workflow on Summit based on micro-computed tomography data. Computational elements include: 1) reconstruction of volumetric image data; 2) denoising with deep neural networks; and 3) non-local means based segmentation and quantitative analysis.

Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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Acknowledgment

This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. An award of computer time was provided by the Frontier Center for Accelerated Application Readiness and the Summit Director’s Discretionary Program. This research also used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.

Author information

Authors and Affiliations

  1. Virginia Tech, Blacksburg, VA, 24060, USA

    James E. McClure

  2. Oak Ridge National Laboratory, Oak Ridge, USA

    Junqi Yin, Ketan C. Maheshwari, Sean Wilkinson, Lucas Vlcek & Mark A. Berrill

  3. University of New South Wales, Sydney, Australia

    Ryan T. Armstrong & Ying Da Wang

  4. Center for Advanced Radiation Sources, University of Chicago, Chicago, IL, USA

    Mark Rivers

Authors
  1. James E. McClure
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  2. Junqi Yin
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  3. Ryan T. Armstrong
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  4. Ketan C. Maheshwari
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  5. Sean Wilkinson
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  6. Lucas Vlcek
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  7. Ying Da Wang
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  8. Mark A. Berrill
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  9. Mark Rivers
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Corresponding author

Correspondence to James E. McClure .

Editor information

Editors and Affiliations

  1. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Jeffrey Nichols

  2. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Becky Verastegui

  3. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Arthur ‘Barney’ Maccabe

  4. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Oscar Hernandez

  5. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Suzanne Parete-Koon

  6. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Theresa Ahearn

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McClure, J.E. et al. (2020). Toward Real-Time Analysis of Synchrotron Micro-Tomography Data: Accelerating Experimental Workflows with AI and HPC. In: Nichols, J., Verastegui, B., Maccabe, A.‘., Hernandez, O., Parete-Koon, S., Ahearn, T. (eds) Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI. SMC 2020. Communications in Computer and Information Science, vol 1315. Springer, Cham. https://doi.org/10.1007/978-3-030-63393-6_15

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  • DOI: https://doi.org/10.1007/978-3-030-63393-6_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63392-9

  • Online ISBN: 978-3-030-63393-6

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