Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

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.

Abstract not provided.

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.

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.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Conventional CPU-based algorithms for Computed Tomography reconstruction lack the computational efficiency necessary to process large, industrial datasets in a reasonable amount of time. Specifically, processing time for a single-pass, trillion volumetric pixel (voxel) reconstruction requires months to reconstruct using a high performance CPU-based workstation. An optimized, single workstation multi-GPU approach has shown performance increases by 2-3 orders-of-magnitude; however, reconstruction of future-size, trillion voxel datasets can still take an entire day to complete. This paper details an approach that further decreases runtime and allows for more diverse workstation environments by using a cluster of GPU-capable workstations. Due to the irregularity of the reconstruction tasks throughout the volume, using a cluster of multi-GPU nodes requires inventive topological structuring and data partitioning to avoid network bottlenecks and achieve optimal GPU utilization. This paper covers the cluster layout and non-linear weighting scheme used in this high-performance multi-GPU CT reconstruction algorithm and presents experimental results from reconstructing two large-scale datasets to evaluate this approach's performance and applicability to future-size datasets. Specifically, our approach yields up to a 20 percent improvement for large-scale data.

This exploratory work investigates the feasibility of extracting linear attenuation functions with respect to energy from a multi-channel radiograph of an object of interest composed of a homogeneous material by simulating the entire imaging system combined with a digital phantom of the object of interest and leveraging this information along with the acquired multi-channel image. This synergistic combination of information allows for improved estimates on not only the attenuation for an effective energy, but for the entire spectrum of energy that is coincident with the detector elements. Material composition identification from radiographs would have wide applications in both medicine and industry. This work will focus on industrial radiography applications and will analyse a range of materials that vary in attenuative properties. This work shows that using iterative solvers holds encouraging potential to fully solve for the linear attenuation profile for the object and material of interest when the imaging system is characterized with respect to initial source x-ray energy spectrum, scan geometry, and accurate digital phantom.

Abstract not provided.

Abstract not provided.

Abstract not provided.

This work will investigate the imaging capabilities of the Multix multi-channel linear array detector and its potential suitability for big-data industrial and security applications versus that which is currently deployed. Multi-channel imaging data holds huge promise in not only finer resolution in materials classification, but also in materials identification and elevated data quality for various radiography and computed tomography applications. The potential pitfall is the signal quality contained within individual channels as well as the required exposure and acquisition time necessary to obtain images comparable to those of traditional configurations. This work will present results of these detector technologies as they pertain to a subset of materials of interest to the industrial and security communities; namely, water, copper, lead, polyethylene, and tin.

Abstract not provided.

Abstract not provided.

We are studying PMDI polyurethane with a fast catalyst, such that filling and polymerization occur simultaneously. The foam is over-packed to tw ice or more of its free rise density to reach the density of interest. Our approach is to co mbine model development closely with experiments to discover new physics, to parameterize models and to validate the models once they have been developed. The model must be able to repres ent the expansion, filling, curing, and final foam properties. PMDI is chemically blown foam, wh ere carbon dioxide is pr oduced via the reaction of water and isocyanate. The isocyanate also re acts with polyol in a competing reaction, which produces the polymer. A new kinetic model is developed and implemented, which follows a simplified mathematical formalism that decouple s these two reactions. The model predicts the polymerization reaction via condensation chemis try, where vitrification and glass transition temperature evolution must be included to correctly predict this quantity. The foam gas generation kinetics are determined by tracking the molar concentration of both water and carbon dioxide. Understanding the therma l history and loads on the foam due to exothermicity and oven heating is very important to the results, since the kinetics and ma terial properties are all very sensitive to temperature. The conservation eq uations, including the e quations of motion, an energy balance, and thr ee rate equations are solved via a stabilized finite element method. We assume generalized-Newtonian rheology that is dependent on the cure, gas fraction, and temperature. The conservation equations are comb ined with a level set method to determine the location of the free surface over time. Results from the model are compared to experimental flow visualization data and post-te st CT data for the density. Seve ral geometries are investigated including a mock encapsulation part, two configur ations of a mock stru ctural part, and a bar geometry to specifically test the density model. We have found that the model predicts both average density and filling profiles well. However, it under predicts density gradients, especially in the gravity direction. Thoughts on m odel improvements are also discussed.

Abstract not provided.

Abstract not provided.