Detection of improvised explosive devices at long-range using coded aperture imaging of backscattered X-rays with dynamic reconstruction

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2009. === Cataloged from PDF version of thesis. === Includes bibliographical references (p. 117-118). === Standoff detection of improvised explosive devices (IEDs) is a continuing problem for the U.S. mil...

Full description

Bibliographic Details
Main Author: Bell, Jayna T. (Jayna Teresa)
Other Authors: Richard C. Lanza and Jacquelyn C. Yanch.
Format: Others
Language:English
Published: Massachusetts Institute of Technology 2010
Subjects:
Online Access:http://hdl.handle.net/1721.1/53279
id ndltd-MIT-oai-dspace.mit.edu-1721.1-53279
record_format oai_dc
spelling ndltd-MIT-oai-dspace.mit.edu-1721.1-532792019-05-02T15:39:33Z Detection of improvised explosive devices at long-range using coded aperture imaging of backscattered X-rays with dynamic reconstruction Detection of IEDs at long-range using CAI of backscattered X-rays with DR Bell, Jayna T. (Jayna Teresa) Richard C. Lanza and Jacquelyn C. Yanch. Massachusetts Institute of Technology. Dept. of Nuclear Science and Engineering. Massachusetts Institute of Technology. Dept. of Nuclear Science and Engineering. Nuclear Science and Engineering. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2009. Cataloged from PDF version of thesis. Includes bibliographical references (p. 117-118). Standoff detection of improvised explosive devices (IEDs) is a continuing problem for the U.S. military. Current X-ray detection systems cannot detect explosives at distances above a few meters and with a source-detector system moving in relation to the target. The aim of this study is to determine the feasibility of a large-area, Coded-Aperture Imaging (CAI) system using X-Ray backscatter as the source of radiation. A moving source-detector system required development of a new reconstruction technique, dynamic reconstruction (DR), which continually back-projects detected events on an event-by-event basis. This research imaged multiple low-Z (polyethylene and water-filled), area targets with backscattered X-rays using standard medical imaging equipment, coded aperture masks with ideal bi-level autocorrelation properties, and dynamic reconstruction (DR). Lower fill factor apertures were the primary metric investigated because contrast was shown to be inversely related to the mask's percentage of open area. This study experimentally determined the optimal mask fill factor, gamma camera imaging protocols, and experimental geometry by examining the resulting effects on image quality. Reconstructed images were analyzed for Contrast-to-noise ratio (CNR), Signal-to-noise Ratio (SNR), resolution, sharpness, the uniformity of the background (artifacts). In addition to changing the fill factor, additional methods of improving the contrast included changing the experimental geometry, reducing the X-ray tube filtration, and widening the X-ray source's cone beam (FOV). (cont.) 14 studies were performed that found 25% fill factor mask reconstructions had the highest average CNR (14.7), compared to 50% and 12.5% fill factor (CNRs 8.50 and 6.9, respectively) with a system resolution of 25 mm at the target. Thus, this study's techniques confirmed that large-area, low fill factor coded apertures could successfully be used, in conjunction with dynamic reconstruction, to image complex, extended scenes at 5 meters with capabilities of up to 50 meters or more. by Jayna T. Bell. S.M. 2010-03-25T15:25:03Z 2010-03-25T15:25:03Z 2009 2009 Thesis http://hdl.handle.net/1721.1/53279 547364486 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 118 p. application/pdf Massachusetts Institute of Technology
collection NDLTD
language English
format Others
sources NDLTD
topic Nuclear Science and Engineering.
spellingShingle Nuclear Science and Engineering.
Bell, Jayna T. (Jayna Teresa)
Detection of improvised explosive devices at long-range using coded aperture imaging of backscattered X-rays with dynamic reconstruction
description Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2009. === Cataloged from PDF version of thesis. === Includes bibliographical references (p. 117-118). === Standoff detection of improvised explosive devices (IEDs) is a continuing problem for the U.S. military. Current X-ray detection systems cannot detect explosives at distances above a few meters and with a source-detector system moving in relation to the target. The aim of this study is to determine the feasibility of a large-area, Coded-Aperture Imaging (CAI) system using X-Ray backscatter as the source of radiation. A moving source-detector system required development of a new reconstruction technique, dynamic reconstruction (DR), which continually back-projects detected events on an event-by-event basis. This research imaged multiple low-Z (polyethylene and water-filled), area targets with backscattered X-rays using standard medical imaging equipment, coded aperture masks with ideal bi-level autocorrelation properties, and dynamic reconstruction (DR). Lower fill factor apertures were the primary metric investigated because contrast was shown to be inversely related to the mask's percentage of open area. This study experimentally determined the optimal mask fill factor, gamma camera imaging protocols, and experimental geometry by examining the resulting effects on image quality. Reconstructed images were analyzed for Contrast-to-noise ratio (CNR), Signal-to-noise Ratio (SNR), resolution, sharpness, the uniformity of the background (artifacts). In addition to changing the fill factor, additional methods of improving the contrast included changing the experimental geometry, reducing the X-ray tube filtration, and widening the X-ray source's cone beam (FOV). === (cont.) 14 studies were performed that found 25% fill factor mask reconstructions had the highest average CNR (14.7), compared to 50% and 12.5% fill factor (CNRs 8.50 and 6.9, respectively) with a system resolution of 25 mm at the target. Thus, this study's techniques confirmed that large-area, low fill factor coded apertures could successfully be used, in conjunction with dynamic reconstruction, to image complex, extended scenes at 5 meters with capabilities of up to 50 meters or more. === by Jayna T. Bell. === S.M.
author2 Richard C. Lanza and Jacquelyn C. Yanch.
author_facet Richard C. Lanza and Jacquelyn C. Yanch.
Bell, Jayna T. (Jayna Teresa)
author Bell, Jayna T. (Jayna Teresa)
author_sort Bell, Jayna T. (Jayna Teresa)
title Detection of improvised explosive devices at long-range using coded aperture imaging of backscattered X-rays with dynamic reconstruction
title_short Detection of improvised explosive devices at long-range using coded aperture imaging of backscattered X-rays with dynamic reconstruction
title_full Detection of improvised explosive devices at long-range using coded aperture imaging of backscattered X-rays with dynamic reconstruction
title_fullStr Detection of improvised explosive devices at long-range using coded aperture imaging of backscattered X-rays with dynamic reconstruction
title_full_unstemmed Detection of improvised explosive devices at long-range using coded aperture imaging of backscattered X-rays with dynamic reconstruction
title_sort detection of improvised explosive devices at long-range using coded aperture imaging of backscattered x-rays with dynamic reconstruction
publisher Massachusetts Institute of Technology
publishDate 2010
url http://hdl.handle.net/1721.1/53279
work_keys_str_mv AT belljaynatjaynateresa detectionofimprovisedexplosivedevicesatlongrangeusingcodedapertureimagingofbackscatteredxrayswithdynamicreconstruction
AT belljaynatjaynateresa detectionofiedsatlongrangeusingcaiofbackscatteredxrayswithdr
_version_ 1719025638509117440