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

Proceedings of SPIE - The International Society for Optical Engineering

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

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.

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TYPE Conference Poster YEAR 2018
ScopusOSTI

Material identification with multichannel radiographs

AIP Conference Proceedings

Collins, Noelle M.; Jimenez, Edward S.; Thompson, Kyle R.

This work aims to validate previous exploratory work done to characterize materials by matching their attenuation profiles using a multichannel radiograph given an initial energy spectrum. The experiment was performed in order to evaluate the effects of noise on the resulting attenuation profiles, which was ignored in simulation. Spectrum measurements have also been collected from various materials of interest. Additionally, a MATLAB optimization algorithm has been applied to these candidate spectrum measurements in order to extract an estimate of the attenuation profile. Being able to characterize materials through this nondestructive method has an extensive range of applications for a wide variety of fields, including quality assessment, industry, and national security.

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TYPE Conference Poster YEAR 2017
ScopusOSTIDOI

Leveraging multi-channel x-ray detector technology to improve quality metrics for industrial and security applications

Proceedings of SPIE - The International Society for Optical Engineering

Jimenez, Edward S.; Thompson, Kyle R.; Stohn, Adriana S.; Goodner, Ryan N.

Sandia National Laboratories has recently developed the capability to acquire multi-channel radio- graphs for multiple research and development applications in industry and security. This capability allows for the acquisition of x-ray radiographs or sinogram data to be acquired at up to 300 keV with up to 128 channels per pixel. This work will investigate whether multiple quality metrics for computed tomography can actually benefit from binned projection data compared to traditionally acquired grayscale sinogram data. Features and metrics to be evaluated include the ability to dis- tinguish between two different materials with similar absorption properties, artifact reduction, and signal-to-noise for both raw data and reconstructed volumetric data. The impact of this technology to non-destructive evaluation, national security, and industry is wide-ranging and has to potential to improve upon many inspection methods such as dual-energy methods, material identification, object segmentation, and computer vision on radiographs.

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TYPE Conference Poster YEAR 2017
ScopusOSTIDOI
Results 26–50 of 104
Results 26–50 of 104