Unsupervised learning methods to perform material identification tasks on spectral computed tomography data

Isabel O. Gallegos; Srivathsan Koundinyan; April N. Suknot; Edward S. Jimenez; Kyle R. Thompson; Ryan N. Goodner

doi:10.1117/12.2326394

11 September 2018 Unsupervised learning methods to perform material identification tasks on spectral computed tomography data

Isabel O. Gallegos, Srivathsan Koundinyan, April N. Suknot, Edward S. Jimenez, Kyle R. Thompson, Ryan N. Goodner

Author Affiliations +

Proceedings Volume 10763, Radiation Detectors in Medicine, Industry, and National Security XIX; 107630G (2018) https://doi.org/10.1117/12.2326394
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

Sandia National Laboratories has developed a method that applies machine learning methods to high-energy spectral x-ray computed tomography data to identify material composition for every reconstructed voxel in the field-of-view. While initial experiments led by Koundinyan et al. demonstrated that supervised machine learning techniques perform well in identifying a variety of classes of materials, this work presents an unsupervised approach that differentiates isolated materials with highly similar properties, and can be applied on spectral computed tomography data to identify materials more accurately compared to traditional performance. Additionally, if regions of the spectrum for multiple voxels become unusable due to artifacts, this method can still reliably perform material identification. This enhanced capability can tremendously impact fields in security, industry, and medicine that leverage non-destructive evaluation for detection, verification, and validation applications.

Conference Presentation

Citation Download Citation

Isabel O. Gallegos, Srivathsan Koundinyan, April N. Suknot, Edward S. Jimenez, Kyle R. Thompson, and Ryan N. Goodner "Unsupervised learning methods to perform material identification tasks on spectral computed tomography data", Proc. SPIE 10763, Radiation Detectors in Medicine, Industry, and National Security XIX, 107630G (11 September 2018); https://doi.org/10.1117/12.2326394

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