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Static coded illumination strategies for low-dose x-ray material decomposition

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Abstract

Static coded aperture x-ray tomography was introduced recently where a static illumination pattern is used to interrogate an object with a low radiation dose, from which an accurate 3D reconstruction of the object can be attained computationally. Rather than continuously switching the pattern of illumination with each view angle, as traditionally done, static code computed tomography (CT) places a single pattern for all views. The advantages are many, including the feasibility of practical implementation. This paper generalizes this powerful framework to develop single-scan dual-energy coded aperture spectral tomography that enables material characterization at a significantly reduced exposure level. Two sensing strategies are explored: rapid kV switching with a single-static block/unblock coded aperture, and coded apertures with non-uniform thickness. Both systems rely on coded illumination with a plurality of x-ray spectra created by kV switching or 3D coded apertures. The structured x-ray illumination is projected through the objects of interest and measured with standard x-ray energy integrating detectors. Then, based on the tensor representation of projection data, we develop an algorithm to estimate a full set of synthesized measurements that can be used with standard reconstruction algorithms to accurately recover the object in each energy channel. Simulation and experimental results demonstrate the effectiveness of the proposed cost-effective solution to attain material characterization in low-dose dual-energy CT.

© 2022 Optica Publishing Group

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References

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Data Availability

The data underlying the results presented in Section 5 are available in Ref. [28]. A description of the data and how to use it can be found in Ref. [24]. The datasets are also available from the corresponding author upon reasonable request.

28. G. Arce, “Walnutdatabase,” University of Delaware, Department of Electrical & Computer Engineering, 2019, https://www.eecis.udel.edu/~arce/Walnut_Database/.

24. A. Cuadros, “ Sparse sampling in x-ray computed tomography via spatial and spectral coded illumination,” Ph.D. thesis (University of Delaware, 2020).

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