Sparse view Compton scatter tomography with energy resolved data: experimental and simulation results

Abdulla Desmal; Brian H. Tracey; Hamideh Rezaee; Eric L. Miller; Jeffrey R. Schubert; Jeff Denker; Aaron Couture

doi:10.1117/12.2266688

1 May 2017 Sparse view Compton scatter tomography with energy resolved data: experimental and simulation results

Abdulla Desmal, Brian H. Tracey, Hamideh Rezaee, Eric L. Miller, Jeffrey R. Schubert, Jeff Denker, Aaron Couture

Proceedings Volume 10187, Anomaly Detection and Imaging with X-Rays (ADIX) II; 1018707 (2017) https://doi.org/10.1117/12.2266688
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

X-ray inspection systems play a critical role in many non-destructive testing and security applications, with systems typically measuring attenuation during transmission along straight-line paths connecting sources and detectors. Computed tomography (CT) systems can provide higher-quality images than single- or dual-view systems, but the need to measure many projections through the scene increases system complexity and cost. We seek to maximize the image quality of sparse-view (few-view) systems by combining attenuation data with measurements of Compton-scattered photons, that deflect after scattering and arrive at detectors via broken ray paths that provide additional sampling of the scene. The work below presents experimental validation of a singlescatter forward model for Compton-scatter data measured with energy-resolving detectors, and demonstrates a reconstruction algorithm that combines both attenuation and scatter measurements. The results suggest that including Compton-scattered data in the reconstruction process can improve image quality for few-view systems.

Conference Presentation

Citation Download Citation

Abdulla Desmal, Brian H. Tracey, Hamideh Rezaee, Eric L. Miller, Jeffrey R. Schubert, Jeff Denker, and Aaron Couture "Sparse view Compton scatter tomography with energy resolved data: experimental and simulation results", Proc. SPIE 10187, Anomaly Detection and Imaging with X-Rays (ADIX) II, 1018707 (1 May 2017); https://doi.org/10.1117/12.2266688

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