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.
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.
Foams and encapsulants serve important roles in the protection of the components they surround. These low density materials may be used to provide shock protection, to protect against high voltage breakdown, or to minimize thermal fluctuations. Voids and gaps in the material, delaminations from a mating material, or non-uniformities in the encapsulating materials can lead to critical failures in the encapsulated component. Despite the important role these low density materials serve, traditional non-destructive inspection tools are limited in their ability to study this material set, especially in the presence of high density materials such as wires. The default approach has been destructive post-mortums where components are deconstructed after a failure and cause and effect are difficult to distinguish. X-ray phase contrast imaging has a longer history at synchrotrons, but this is not a realistic solution for non-destructive inspection. We have demonstrated grating-based x-ray phase contrast 3-D tomography in a laboratory environment with a conventional x-ray tube. Our large format grating fabrication capability enables imaging with large fields of view (10 cm2) at 28 keV for the successful non-destructive inspection of these low-density materials. We demonstrate that the complementary image modalities available with XPCI provide unique information and higher contrast for the inspection of defects in low density materials than conventional x-ray alone.
KEYWORDS: Computer security, Radiography, Data acquisition, Sensors, Absorption, Nondestructive evaluation, X-rays, Computed tomography, Interfaces, Signal to noise ratio
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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