Abstract: Coded aperture Xray computed tomography (CT) has the potential to revolutionize Xray tomography systems in medical imaging and air and rail transit security  both areas of global importance. It allows either a reduced set of measurements in Xray CT without degrada tion in image reconstruction, or measure multiplexed Xrays to simplify the sensing geometry. Measurement reduction is of particular interest in medical imaging to reduce radiation, and airport security often imposes practical constraints leading to limited angle geometries. Coded aperture compressive Xray CT places a coded aperture pattern in front of the Xray source in order to obtain patterned projections onto a detector. Compressive sensing (CS) reconstruction algorithms are then used to recover the image. To date, the coded illumination patterns used in conventional CT systems have been random. This paper addresses the code optimization prob lem for general tomography imaging based on the point spread function (PSF) of the system, which is used as a measure of the sensing matrix quality which connects to the restricted isom etry property (RIP) and coherence of the sensing matrix. The methods presented are general, simple to use, and can be easily extended to other imaging systems. Simulations are presented wheremore »
PhysicsConstrained Dictionary Learning for Selective Laser Melting Process Monitoring
Compressed sensing (CS) as a new data acquisition technique has been applied to monitor manufacturing processes. With a few measurements, sparse coefficient vectors can be recovered by solving an inverse problem and original signals can be reconstructed. Dictionary learning methods have been developed and applied in combination with CS to improve the sparsity level of the recovered coefficient vectors. In this work, a physicsconstrained dictionary learning approach is proposed to solve both of reconstruction and classification problems by optimizing measurement, basis, and classification matrices simultaneously with the considerations of the applicationspecific restrictions. It is applied in image acquisitions in selective laser melting (SLM). The proposed approach includes the optimization in two steps. In the first stage, with the basis matrix fixed, the measurement matrix is optimized by determining the pixel locations for sampling in each image. The optimized measurement matrix only includes one nonzero entry in each row. The optimization of pixel locations is solved based on a constrained FrameSense algorithm. In the second stage, with the measurement matrix fixed, the basis and classification matrices are optimized based on the KSVD algorithm. With the optimized basis matrix, the coefficient vector can be recovered with CS. The original signal can be more »
 Award ID(s):
 1663227
 Publication Date:
 NSFPAR ID:
 10282303
 Journal Name:
 Proceedings of 2021 IISE Annual Conference & Expo
 Sponsoring Org:
 National Science Foundation
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Abstract: Coded aperture Xray computed tomography (CT) has the potential to revolutionize Xray tomography systems in medical imaging and air and rail transit security  both areas of global importance. It allows either a reduced set of measurements in Xray CT without degrada tion in image reconstruction, or measure multiplexed Xrays to simplify the sensing geometry. Measurement reduction is of particular interest in medical imaging to reduce radiation, and airport security often imposes practical constraints leading to limited angle geometries. Coded aperture compressive Xray CT places a coded aperture pattern in front of the Xray source in order to obtain patterned projections onto a detector. Compressive sensing (CS) reconstruction algorithms are then used to recover the image. To date, the coded illumination patterns used in conventional CT systems have been random. This paper addresses the code optimization prob lem for general tomography imaging based on the point spread function (PSF) of the system, which is used as a measure of the sensing matrix quality which connects to the restricted isom etry property (RIP) and coherence of the sensing matrix. The methods presented are general, simple to use, and can be easily extended to other imaging systems. Simulations are presented wheremore »

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