Optimization of input parameters to improve big-data computed tomography reconstruction performance (Conference Presentation)

Celia J. Flicker; Edward S. Jimenez

doi:10.1117/12.2275851

25 September 2017 Optimization of input parameters to improve big-data computed tomography reconstruction performance (Conference Presentation)

Celia J. Flicker, Edward S. Jimenez

Author Affiliations +

Proceedings Volume 10393, Radiation Detectors in Medicine, Industry, and National Security XVIII; 103930I (2017) https://doi.org/10.1117/12.2275851
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

This work seeks to develop an autonomous optimization of input computational resource parameters for arbitrary big-data computed tomography (CT) conﬁgurations. It is well known that graphics processing units (GPU) have been a boon to many high-performance applications, including CT. The reconstruction task has both colossal computational and data throughput requirements that easily tax high-end GPUs to their limit. For big-data industrial and research applications, the burden is exacerbated through the use of high pixel count detectors (≥ 16 megapixels) and the large number of projections needed to meet Nyquist sampling requirements, resulting in datasets up to terabytes in size. Previous work has shown that the GPU kernels can be optimized to eﬃciently handle big-data; however, as this work will show, some sensitivities exist with respect to the tunable input parameters that can exact an exaggerated toll on reconstruction performance. This work will investigate the input parameter space for various relevant and future-sized datasets and will present a calibration approach to optimize reconstruction performance for varying sized detectors, geometries, and graphics processing resources. This work has the potential to dramatically improve many non-destructive evaluation and inspection applications in industry, security, and research where reconstruction rate is the main bottleneck of the resource chain. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DEAC04-94AL85000.

Conference Presentation

Citation Download Citation

Celia J. Flicker and Edward S. Jimenez "Optimization of input parameters to improve big-data computed tomography reconstruction performance (Conference Presentation)", Proc. SPIE 10393, Radiation Detectors in Medicine, Industry, and National Security XVIII, 103930I (25 September 2017); https://doi.org/10.1117/12.2275851

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

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
PRESENTATION

WATCH
PRESENTATION SAVE TO MY LIBRARY

GET CITATION

KEYWORDS

Homeland security

Computed tomography

Nondestructive evaluation

Sensors

Calibration

Graphics processing units

Inspection

Show All Keywords

Keywords/Phrases

Search In:

Publication Years