Advanced Image Reconstruction for MCP Detector in Event Mode

Chen Zhang; Zachary Morgan

doi:10.1007/978-3-030-96498-6_22

Advanced Image Reconstruction for MCP Detector in Event Mode

Chen Zhang, Zachary Morgan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

A two-step data reduction framework is proposed in this study to reconstruct a radiograph from the data collected with a micro-channel plate (MCP) detector operating under event mode. One clustering algorithm and three neutron event back-tracing models are proposed and evaluated using both example data and a full scan data. The reconstructed radiographs are analyzed, the results of which are used to suggest future development.

Original language	English
Title of host publication	Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers
Editors	[given-name]Jeffrey Nichols, [given-name]Arthur ‘Barney’ Maccabe, James Nutaro, Swaroop Pophale, Pravallika Devineni, Theresa Ahearn, Becky Verastegui
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	383-397
Number of pages	15
ISBN (Print)	9783030964979
DOIs	https://doi.org/10.1007/978-3-030-96498-6_22
State	Published - 2022
Event	21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021 - Virtual, Online Duration: Oct 18 2021 → Oct 20 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1512 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021
City	Virtual, Online
Period	10/18/21 → 10/20/21

Funding

A portion of this research used resources at the SNS, a Department of Energy (DOE) Office of Science User Facility operated by ORNL. ORNL is managed by UT-Battelle LLC for DOE under Contract DE-AC05-00OR22725. Acknowledgements. A portion of this research used resources at the SNS, a Department of Energy (DOE) Office of Science User Facility operated by ORNL. ORNL is managed by UT-Battelle LLC for DOE under Contract DE-AC05-00OR22725. 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).

Keywords

DBSCAN
Image reconstruction
MCP detector

Access to Document

10.1007/978-3-030-96498-6_22

Cite this

Zhang, C., & Morgan, Z. (2022). Advanced Image Reconstruction for MCP Detector in Event Mode. In J. Nichols, A. B. Maccabe, J. Nutaro, S. Pophale, P. Devineni, T. Ahearn, & B. Verastegui (Eds.), Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers (pp. 383-397). (Communications in Computer and Information Science; Vol. 1512 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-96498-6_22

Zhang, Chen ; Morgan, Zachary. / Advanced Image Reconstruction for MCP Detector in Event Mode. Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers. editor / [given-name]Jeffrey Nichols ; [given-name]Arthur ‘Barney’ Maccabe ; James Nutaro ; Swaroop Pophale ; Pravallika Devineni ; Theresa Ahearn ; Becky Verastegui. Springer Science and Business Media Deutschland GmbH, 2022. pp. 383-397 (Communications in Computer and Information Science).

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title = "Advanced Image Reconstruction for MCP Detector in Event Mode",

abstract = "A two-step data reduction framework is proposed in this study to reconstruct a radiograph from the data collected with a micro-channel plate (MCP) detector operating under event mode. One clustering algorithm and three neutron event back-tracing models are proposed and evaluated using both example data and a full scan data. The reconstructed radiographs are analyzed, the results of which are used to suggest future development.",

keywords = "DBSCAN, Image reconstruction, MCP detector",

author = "Chen Zhang and Zachary Morgan",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021 ; Conference date: 18-10-2021 Through 20-10-2021",

year = "2022",

doi = "10.1007/978-3-030-96498-6_22",

language = "English",

isbn = "9783030964979",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

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Zhang, C & Morgan, Z 2022, Advanced Image Reconstruction for MCP Detector in Event Mode. in J Nichols, AB Maccabe, J Nutaro, S Pophale, P Devineni, T Ahearn & B Verastegui (eds), Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers. Communications in Computer and Information Science, vol. 1512 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 383-397, 21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021, Virtual, Online, 10/18/21. https://doi.org/10.1007/978-3-030-96498-6_22

Advanced Image Reconstruction for MCP Detector in Event Mode. / Zhang, Chen ; Morgan, Zachary.
Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers. ed. / [given-name]Jeffrey Nichols; [given-name]Arthur ‘Barney’ Maccabe; James Nutaro; Swaroop Pophale; Pravallika Devineni; Theresa Ahearn; Becky Verastegui. Springer Science and Business Media Deutschland GmbH, 2022. p. 383-397 (Communications in Computer and Information Science; Vol. 1512 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - A two-step data reduction framework is proposed in this study to reconstruct a radiograph from the data collected with a micro-channel plate (MCP) detector operating under event mode. One clustering algorithm and three neutron event back-tracing models are proposed and evaluated using both example data and a full scan data. The reconstructed radiographs are analyzed, the results of which are used to suggest future development.

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Zhang C , Morgan Z. Advanced Image Reconstruction for MCP Detector in Event Mode. In Nichols J, Maccabe AB, Nutaro J, Pophale S, Devineni P, Ahearn T, Verastegui B, editors, Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 383-397. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-96498-6_22

Advanced Image Reconstruction for MCP Detector in Event Mode

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