Quantitative Mass Density Image Reconstructed from the Complex X-Ray Refractive Index

We demonstrate a new analytical X-ray computed tomography technique for visualizing and quantifying the mass density of materials comprised of low atomic number elements with unknown atomic ratios. The mass density was obtained from the experimentally observed ratio of the imaginary and real parts o...

Full description

Saved in:
Bibliographic Details
Published inPloS one Vol. 10; no. 6; p. e0131401
Main Authors Mukaide, Taihei, Iida, Atsuo, Watanabe, Masatoshi, Takada, Kazuhiro, Noma, Takashi
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 26.06.2015
Public Library of Science (PLoS)
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:We demonstrate a new analytical X-ray computed tomography technique for visualizing and quantifying the mass density of materials comprised of low atomic number elements with unknown atomic ratios. The mass density was obtained from the experimentally observed ratio of the imaginary and real parts of the complex X-ray refractive index. An empirical linear relationship between the X-ray mass attenuation coefficient of the materials and X-ray energy was found for X-ray energies between 8 keV and 30 keV. The mass density image of two polymer fibers was quantified using the proposed technique using a scanning-type X-ray microbeam computed tomography system equipped with a wedge absorber. The reconstructed mass density agrees well with the calculated one.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Competing Interests: Taihei Mukaide, Masatoshi Watanabe, Kazuhiro Takada, and Takashi Noma are employees at Canon Inc. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.
Conceived and designed the experiments: TM. Performed the experiments: TM AI MW KT TN. Analyzed the data: TM AI. Contributed reagents/materials/analysis tools: TM MW. Wrote the paper: TM AI.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0131401