Coding and sampling for compressive x-ray diffraction tomography

Joel A. Greenberg; Kalyani Krishnamurthy; Manu Lakshmanan; Kenneth MacCabe; Scott Wolter; Anuj Kapadia; David Brady

doi:10.1117/12.2027128

26 September 2013 Coding and sampling for compressive x-ray diffraction tomography

Joel A. Greenberg, Kalyani Krishnamurthy, Manu Lakshmanan, Kenneth MacCabe, Scott Wolter, Anuj Kapadia, David Brady

Author Affiliations +

Proceedings Volume 8858, Wavelets and Sparsity XV; 885813 (2013) https://doi.org/10.1117/12.2027128
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

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

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

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