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Proceedings Article

Coding and sampling for compressive x-ray diffraction tomography

[+] Author Affiliations
Joel A. Greenberg, Kalyani Krishnamurthy, Manu Lakshmanan, Kenneth MacCabe, Scott Wolter, Anuj Kapadia, David Brady

Duke Univ. (United States)

Proc. SPIE 8858, Wavelets and Sparsity XV, 885813 (September 26, 2013); doi:10.1117/12.2027128
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From Conference Volume 8858

  • Wavelets and Sparsity XV
  • Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis
  • San Diego, California, United States | August 25, 2013

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 © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Joel A. Greenberg ; Kalyani Krishnamurthy ; Manu Lakshmanan ; Kenneth MacCabe ; Scott Wolter, et al.
" Coding and sampling for compressive x-ray diffraction tomography ", Proc. SPIE 8858, Wavelets and Sparsity XV, 885813 (September 26, 2013); doi:10.1117/12.2027128; http://dx.doi.org/10.1117/12.2027128


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