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

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

Citation Download Citation

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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