Publications

Lee, Dennis J.; Mulcahy-Stanislawczyk, Johnathan M.; Jimenez, Edward S.; Goodner, Ryan N.; West, Roger D.; Epstein, Collin E.; Thompson, Kyle R.; Dagel, Amber L.

Abstract not provided.

Dagel, Amber L.; Epstein, Collin E.; Finnegan, Patrick S.; Goodner, Ryan N.; Grover, Steven G.; Kernen, Burke L.; Mulcahy-Stanislawczyk, Johnathan M.; Thompson, Kyle R.; West, Roger D.

Abstract not provided.

Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

Epstein, Collin E.; Goodner, Ryan N.; West, Roger D.; Thompson, Kyle R.; Dagel, Amber L.

X-ray phase contrast imaging (XPCI) is a nondestructive evaluation technique that enables high-contrast detection of low-attenuation materials that are largely transparent in traditional radiography. Extending a grating-based Talbot-Lau XPCI system to three-dimensional imaging with computed tomography (CT) imposes two motion requirements: the analyzer grating must translate transverse to the optical axis to capture image sets for XPCI reconstruction, and the sample must rotate to capture angular data for CT reconstruction. The acquisition algorithm choice determines the order of movement and positioning of the two stages. The choice of the image acquisition algorithm for XPCI CT is instrumental to collecting high fidelity data for reconstruction. We investigate how data acquisition influences XPCI CT by comparing two simple data acquisition algorithms and determine that capturing a full phase-stepping image set for a CT projection before rotating the sample results in higher quality data.

Dagel, Amber L.; Epstein, Collin E.; Thompson, Kyle R.; Goodner, Ryan N.; Grover, Steven M.; West, Roger D.

Abstract not provided.

Fayad, Samuel S.; Goodner, Ryan N.; Quintana, Enrico C.

Abstract not provided.

Epstein, Collin E.; Goodner, Ryan N.; Thompson, Kyle R.; Dagel, Amber L.

Abstract not provided.

Dagel, Amber L.; Epstein, Collin E.; Thompson, Kyle R.; Goodner, Ryan N.; Grover, Steven M.; West, Roger D.

Abstract not provided.

Epstein, Collin E.; Goodner, Ryan N.; Thompson, Kyle R.; Dagel, Amber L.

Abstract not provided.

Dagel, Amber L.; West, Roger D.; Goodner, Ryan N.; Grover, Steven M.; Epstein, Collin E.; Thompson, Kyle R.

Abstract not provided.

Proceedings of SPIE - The International Society for Optical Engineering

Dagel, Amber L.; West, Roger D.; Goodner, Ryan N.; Grover, Steven M.; Epstein, Collin E.; Thompson, Kyle R.

High-quality image products in an X-Ray Phase Contrast Imaging (XPCI) system can be produced with proper system hardware and data acquisition. However, it may be possible to further increase the quality of the image products by addressing subtleties and imperfections in both hardware and the data acquisition process. Noting that addressing these issues entirely in hardware and data acquisition may not be practical, a more prudent approach is to determine the balance of how the apparatus may reasonably be improved and what can be accomplished with image post-processing techniques. Given a proper signal model for XPCI data, image processing techniques can be developed to compensate for many of the image quality degradations associated with higher-order hardware and data acquisition imperfections. However, processing techniques also have limitations and cannot entirely compensate for sub-par hardware or inaccurate data acquisition practices. Understanding system and image processing technique limitations enables balancing between hardware, data acquisition, and image post-processing. In this paper, we present some of the higher-order image degradation effects we have found associated with subtle imperfections in both hardware and data acquisition. We also discuss and demonstrate how a combination of hardware, data acquisition processes, and image processing techniques can increase the quality of XPCI image products. Finally, we assess the requirements for high-quality XPCI images and propose reasonable system hardware modifications and the limits of certain image processing techniques.

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

Abstract not provided.

Fayad, Samuel S.; Reu, Phillip L.; Goodner, Ryan N.

Abstract not provided.

Thompson, Kyle R.; Dagel, Amber L.; Goodner, Ryan N.; Epstein, Collin E.

Abstract not provided.

Gucik, Mary L.; Biedermann, Laura B.; Jones, Brad H.; Arrington, Christian L.; Goeke, Ronald S.; Goodner, Ryan N.; Lambert, Timothy N.; Sinclair, Michael B.; Wheeler, David R.

Abstract not provided.

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

Abstract not provided.

Thompson, Kyle R.; Dagel, Amber L.; Goodner, Ryan N.

Abstract not provided.

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

Abstract not provided.

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

Abstract not provided.

Dagel, Amber L.; Arrington, Christian L.; Finnegan, Patrick S.; Goodner, Ryan N.; Hollowell, Andrew E.; Thompson, Kyle R.

Abstract not provided.

Holswade, Erica A.; Lopez, Elizabeth L.; Goodner, Ryan N.; Thompson, Kyle R.; Jimenez, Edward S.

Abstract not provided.

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.

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

Abstract not provided.

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.