X-ray image acquisition of a device undergoing pyroshock [Slides]
Abstract not provided.
Publications
AirNet-SNL: End-To-End Training of Iterative Reconstruction and Deep Neural Network Regularization for Sparse-Data XPCI CT
Abstract not provided.
Mask design and fabrication for higher energy speckle-based XPCI
Abstract not provided.
AirNet-SNL: End-to-end training of iterative reconstruction and deep neural network regularization for sparse-data XPCI CT
Optics InfoBase Conference Papers
We present a deep learning image reconstruction method called AirNet-SNL for sparse view computed tomography. It combines iterative reconstruction and convolutional neural networks with end-to-end training. Our model reduces streak artifacts from filtered back-projection with limited data, and it trains on randomly generated shapes. This work shows promise to generalize learning image reconstruction.
Influence of Data Acquisition Algorithms on X-Ray Phase Contrast Imaging Computed Tomography
Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
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.
Phase Retrieval using Consensus Equilibrium in X-Ray Phase Contrast Imaging
Abstract not provided.
The Effects of Thermal Expansion of Gratings in Talbot-Lau Lab-Based X-Ray Phase Contrast Imaging
Abstract not provided.
Novel Plating Approaches for Grating Fabrication: Conformal Deposition Through Mask Filling and Superfilled Electrodeposition
Abstract not provided.
High aspect ratio silicon template fabrication for 100 keV X-ray phase contrast imaging
Abstract not provided.
Machine Learning-Based Image Reconstruction for Undersampled XPCI Datasets
Abstract not provided.
High-Fidelity Calibration and Characterization of a Hyperspectral Computed Tomography System
Abstract not provided.
High-Fidelity Calibration and Characterization of a Hyperspectral Computed Tomography System
Abstract not provided.
Influence of Data Acquisition Algorithms on X-Ray Phase Contrast Imaging Computed Tomography
Abstract not provided.
The Effects of Thermal Expansion of Gratings in Talbot-Lau Lab-Based X-Ray Phase Contrast Imaging
Abstract not provided.
Rapid 3D X-ray Phase Contrast Imaging
Abstract not provided.
Influence of Data Acquisition Algorithms on X-Ray Phase Contrast Imaging Computed Tomography
Abstract not provided.
Optimization of Hardware and Image Processing for Improved Image Quality in X-Ray Phase Contrast Imaging
Abstract not provided.
Optimization of hardware and image processing for improved image quality in X-ray phase contrast imaging
Proceedings of SPIE - The International Society for Optical Engineering
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.
Non-Destructive Materials Identification System using Spectral Computed Tomography
Abstract not provided.
Improvements at Sandia's High Energy Radiography Facility
Abstract not provided.
Material Identification and Classification using Machine Learning Techniques with Hyperspectral Computed Tomography
Abstract not provided.
Material Identification and Classification using Machine Learning Techniques with Hyperspectral Computed Tomography
Abstract not provided.
Progress on building a laboratory based x-ray phase contrast imaging computed tomography system
Abstract not provided.
Unsupervised Learning Methods to Perform Material Identification Tasks on Spectral Computed Tomography Data
Abstract not provided.
Progress on building a laboratory based x-ray phase contrast imaging computed tomography system
Abstract not provided.
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