Processing of spectral X-ray data with principal components analysis

The goal of the work was to develop a general method for processing spectral x-ray image data. Principle component analysis (PCA) is a well understood technique for multivariate data analysis and so was investigated. To assess this method, spectral (multi-energy) computed tomography (CT) data was ob...

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Detalles Bibliográficos
Autores principales: Butler, A P H, Butler, P H, Cook, N J, Butzer, J, Schleich, N, Tlustos, L, Scott, N, Grasset, R, de Ruiter, N, Anderson, N G
Lenguaje:eng
Publicado: 2011
Materias:
Detectors and Experimental Techniques
Nuclear Physics - XX
XX
Acceso en línea:https://dx.doi.org/10.1016/j.nima.2010.06.149
http://cds.cern.ch/record/1399797
Descripción
Sumario:The goal of the work was to develop a general method for processing spectral x-ray image data. Principle component analysis (PCA) is a well understood technique for multivariate data analysis and so was investigated. To assess this method, spectral (multi-energy) computed tomography (CT) data was obtained using a Medipix2 detector in a MARS-CT (Medipix All Resolution System). PCA was able to separate bone (calcium) from two elements with k-edges in the X-ray spectrum used (iodine and barium) within a mouse. This has potential clinical application in dual-energy CT systems and future Medipix3 based spectral imaging where up to eight energies can be recorded simultaneously with excellent energy resolution. (c) 2010 Elsevier B.V. All rights reserved.

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