Elemental Mass Quantification from Spectral X-ray Radiographs and Fluorescence using Gauss-Newton and Deep Learning Approaches

dc.contributor.advisor Erickson, Anna S.
dc.contributor.author Gillis, Wesley C.
dc.contributor.committeeMember Biegalski, Steven
dc.contributor.committeeMember Hertel, Nolan
dc.contributor.committeeMember Haas, Derek
dc.contributor.committeeMember Pazdernik, Karl
dc.contributor.committeeMember Gilbert, Andrew
dc.contributor.department Mechanical Engineering
dc.date.accessioned 2021-01-11T17:14:40Z
dc.date.available 2021-01-11T17:14:40Z
dc.date.created 2020-12
dc.date.issued 2020-12-14
dc.date.submitted December 2020
dc.date.updated 2021-01-11T17:14:40Z
dc.description.abstract The goal of this thesis is to explore elemental mass quantification from spectral X-ray radiographs and X-ray fluorescence. This would provide a nondestructive technique to the IAEA for international safeguards. The entire work's setup is a 160 kVp X-ray beam incident on a powder and measured with a pixelated spectral CdTe photon detector. First, the work implements a partial-volume correction to an existing numerical approach. An alternative deep learning approach is presented using CNNs to regress elemental mass. The training dataset is generated with Monte Carlo and empirical detector characterization. An unsupervised deep learning approach is also explored for the simulation-to-experiment transformation. The method is tested on both simulation and experimental data. Lastly, X-ray fluorescence from the sample is measured with a second, out-of-beam spectral photon detector. Similarly, deep learning is used to regress elemental mass. This is done both from X-ray fluorescence alone and fused with the spectral radiographic data. The work provides new technology to the IAEA and shows how simulation can be used in deep learning where experimental data is scarce.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/64215
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject X-ray interrogation
dc.subject Material discrimination
dc.subject Elemental analysis
dc.subject mass quantification
dc.subject deep learning
dc.title Elemental Mass Quantification from Spectral X-ray Radiographs and Fluorescence using Gauss-Newton and Deep Learning Approaches
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Erickson, Anna S.
local.contributor.corporatename George W. Woodruff School of Mechanical Engineering
local.contributor.corporatename College of Engineering
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relation.isOrgUnitOfPublication c01ff908-c25f-439b-bf10-a074ed886bb7
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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