Multispectral x-ray CT: multivariate statistical analysis for efficient reconstruction

Mina Kheirabadi, Wail Mustafa, Mark Lyksborg, Ulrik Lund Olsen, Anders Bjorholm Dahl

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Recent developments in multispectral X-ray detectors allow for an efficient identification of materials based on their chemical composition. This has a range of applications including security inspection, which is our motivation. In this paper, we analyze data from a tomographic setup employing the MultiX detector, that records projection data in 128 energy bins covering the range from 20 to 160 keV. Obtaining all information from this data requires reconstructing 128 tomograms, which is computationally expensive. Instead, we propose to reduce the dimensionality of projection data prior to reconstruction and reconstruct from the reduced data. We analyze three linear methods for dimensionality reduction using a dataset with 37 equally-spaced projection angles. Four bottles with different materials are recorded for which we are able to obtain similar discrimination of their content using a very reduced subset of tomograms compared to the 128 tomograms that would otherwise be needed without dimensionality reduction.
Original languageEnglish
Title of host publicationProceedings Volume 10391, Developments in X-Ray Tomography XI
Number of pages11
PublisherSPIE - International Society for Optical Engineering
Publication date2017
Publication statusPublished - 2017
EventSPIE Optics + Photonics 2017 - San Diego, United States
Duration: 6 Aug 201710 Aug 2017


ConferenceSPIE Optics + Photonics 2017
CountryUnited States
CitySan Diego
Internet address
SeriesProceedings of SPIE, the International Society for Optical Engineering

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