Multi-energy penalized maximum-likelihood reconstruction for x-ray security imaging

David G. Politte; Jingwei Lu; Joseph A. O'Sullivan; Eric Johnson; Carl Bosch

doi:10.1117/12.2263343

1 May 2017 Multi-energy penalized maximum-likelihood reconstruction for x-ray security imaging

David G. Politte, Jingwei Lu, Joseph A. O'Sullivan, Eric Johnson, Carl Bosch

Author Affiliations +

Proceedings Volume 10187, Anomaly Detection and Imaging with X-Rays (ADIX) II; 101870D (2017) https://doi.org/10.1117/12.2263343
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

X-ray imaging for security screening is a challenging application that requires simultaneous satisfaction of seemingly incompatible constraints, including low cost, high throughput, and reliable detection of threats. We take a principled computational imaging approach to system design. Mathematical models of the underlying physics and a Huber-class penalty function yield a penalized maximum-likelihood problem. We extend our iterative algorithm for computing linear attenuation coefficients to use multiple energy bins in the SureScan x1000, which has an unconventional, fixed-source geometry. The goal is to maintain the spatial resolution of the single-energy reconstruction while providing information for material characterization used for detection of threats.

Conference Presentation

Citation Download Citation

David G. Politte, Jingwei Lu, Joseph A. O'Sullivan, Eric Johnson, and Carl Bosch "Multi-energy penalized maximum-likelihood reconstruction for x-ray security imaging", Proc. SPIE 10187, Anomaly Detection and Imaging with X-Rays (ADIX) II, 101870D (1 May 2017); https://doi.org/10.1117/12.2263343

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE