Publications

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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.

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.

Many optical systems are used for specific tasks such as classification. Of these systems, the majority are designed to maximize image quality for human observers. However, machine learning classification algorithms do not require the same data representation used by humans. We investigate the compressive optical systems optimized for a specific machine sensing task. Two compressive optical architectures are examined: an array of prisms and neutral density filters where each prism and neutral density filter pair realizes one datum from an optimized compressive sensing matrix, and another architecture using conventional optics to image the aperture onto the detector, a prism array to divide the aperture, and a pixelated attenuation mask in the intermediate image plane. We discuss the design, simulation, and trade-offs of these systems built for compressed classification of the Modified National Institute of Standards and Technology dataset. Both architectures achieve classification accuracies within 3% of the optimized sensing matrix for compression ranging from 98.85% to 99.87%. The performance of the systems with 98.85% compression were between an F / 2 and F / 4 imaging system in the presence of noise.

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Images are often not the optimal data form to perform machine learning tasks such as scene classification. Compressive classification can reduce the size, weight, and power of a system by selecting the minimum information while maximizing classification accuracy.In this work we present designs and simulations of prism arrays which realize sensing matrices using a monolithic element. The sensing matrix is optimized using a neural network architecture to maximize classification accuracy of the MNIST dataset while considering the blurring caused by the size of each prism. Simulated optical hardware performance for a range of prism sizes are reported.

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Sandia National Laboratories is developing a laboratory-based x-ray phase contrast imaging (XPCI) computed tomography (CT) system. This system utilizes a Talbot-Lau interferometer based on in-house fabricated gratings and a conventional x-ray system. Initial work has focused on adding CT capabilities to a 28 keV XPCI system. A new set of gratings tuned for an x-ray energy of 100 keV is being developed. This new grating set will facilitate imaging denser components. System configuration details will be presented as well as a discussion of the challenges associated with building an XPCI CT system. Additionally, initial imaging results will be presented.

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Alternative architectures for imaging devices which fuse the optical design with an algorithmic component enable inexpensive sensing systems optimized for specific classification tasks. Leveraging past work in task-specific compressive devices, this work seeks to improve upon previous designs of optical and algorithmic elements. We achieve this through use of genetic algorithms to enforce conditions upon the optimization phase of a computational imaging system. Through enforcement of binary sampling or discrete-valued outputs of a system measurement matrix, it is possible to simplify optical hardware design while achieving high task-specific performance.

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