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Proceedings Paper

Coding and sampling for compressive x-ray diffraction tomography
Author(s): Joel A. Greenberg; Kalyani Krishnamurthy; Manu Lakshmanan; Kenneth MacCabe; Scott Wolter; Anuj Kapadia; David Brady
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Paper Abstract

Coded apertures and energy resolving detectors may be used to improve the sampling efficiency of x-ray tomography and increase the physical diversity of x-ray phenomena measured. Coding and decompressive inference enable increased molecular specificity, reduced exposure and scan times. We outline a specific coded aperture x-ray coherent scatter imaging architecture that demonstrates the potential of such schemes. Based on this geometry, we develop a physical model using both a semi-analytic and Monte Carlo-based framework, devise an experimental realization of the system, describe a reconstruction algorithm for estimating the object from raw data, and propose a classification scheme for identifying the material composition of the object at each location

Paper Details

Date Published: 26 September 2013
PDF: 11 pages
Proc. SPIE 8858, Wavelets and Sparsity XV, 885813 (26 September 2013); doi: 10.1117/12.2027128
Show Author Affiliations
Joel A. Greenberg, Duke Univ. (United States)
Kalyani Krishnamurthy, Duke Univ. (United States)
Manu Lakshmanan, Duke Univ. (United States)
Kenneth MacCabe, Duke Univ. (United States)
Scott Wolter, Duke Univ. (United States)
Elon Univ. (United States)
Anuj Kapadia, Duke Univ. (United States)
David Brady, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 8858:
Wavelets and Sparsity XV
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)

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