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Development and Optimization of Regularized Tomographic Reconstruction Algorithms Utilizing Equally-Sloped Tomography

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TLDR
Two new algorithms for tomographic reconstruction which incorporate the technique of equally-sloped tomography (EST) and allow for the optimized and flexible implementation of regularization schemes, such as total variation constraints, and the incorporation of arbitrary physical constraints are developed.
Abstract
We develop two new algorithms for tomographic reconstruction which incorporate the technique of equally-sloped tomography (EST) and allow for the optimized and flexible implementation of regularization schemes, such as total variation constraints, and the incorporation of arbitrary physical constraints. The founding structure of the developed algorithms is EST, a technique of tomographic acquisition and reconstruction first proposed by Miao in 2005 for performing tomographic image reconstructions from a limited number of noisy projections in an accurate manner by avoiding direct interpolations. EST has recently been successfully applied to coherent diffraction microscopy, electron microscopy, and computed tomography for image enhancement and radiation dose reduction. However, the bottleneck of EST lies in its slow speed due to its higher computation requirements. In this paper, we formulate the EST approach as a constrained problem and subsequently transform it into a series of linear problems, which can be accurately solved by the operator splitting method. Based on these mathematical formulations, we develop two iterative algorithms for tomographic image reconstructions through EST, which incorporate Bregman and continuative regularization. Our numerical experiment results indicate that the new tomographic image reconstruction algorithms not only significantly reduce the computational time, but also improve the image quality. We anticipate that EST coupled with the novel iterative algorithms will find broad applications in X-ray tomography, electron microscopy, coherent diffraction microscopy, and other tomography fields.

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Journal ArticleDOI

Electron tomography at 2.4-ångström resolution

TL;DR: The experimental demonstration of a general electron tomography method that achieves atomic-scale resolution without initial assumptions about the sample structure is reported, and it is anticipated that this general method can be applied not only to determine the 3D structure of nanomaterials at atomic- scale resolution, but also to improve the spatial resolution and image quality in other tomography fields.
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Compressed sensing electron tomography.

TL;DR: The CS-ET approach enables more reliable quantitative analysis of the reconstructions as well as novel 3D studies from extremely limited data, and robust reconstruction is shown to be possible from far fewer projections than are normally used.
Journal ArticleDOI

Atomic electron tomography: 3D structures without crystals

TL;DR: The combination of AET and atom-tracing algorithms has enabled the determination of the coordinates of individual atoms and point defects in materials with a 3D precision, allowing direct measurements of 3D atomic displacements and the full strain tensor.
Journal ArticleDOI

High-resolution, low-dose phase contrast X-ray tomography for 3D diagnosis of human breast cancers

TL;DR: This work imaged a human breast in three dimensions and identified a malignant cancer with a pixel size of 92 μm and a radiation dose less than that of dual-view mammography, demonstrating that high-resolution 3D diagnostic imaging of human breast cancers can, in principle, be performed at clinical compatible doses.
References
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Book

Numerical Optimization

TL;DR: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization, responding to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.
Journal ArticleDOI

Nonlinear total variation based noise removal algorithms

TL;DR: In this article, a constrained optimization type of numerical algorithm for removing noise from images is presented, where the total variation of the image is minimized subject to constraints involving the statistics of the noise.
Journal ArticleDOI

Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

TL;DR: In this paper, the authors considered the model problem of reconstructing an object from incomplete frequency samples and showed that with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the lscr/sub 1/ minimization problem.
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Principles of Computerized Tomographic Imaging

TL;DR: Properties of Computerized Tomographic Imaging provides a tutorial overview of topics in tomographic imaging covering mathematical principles and theory and how to apply the theory to problems in medical imaging and other fields.
Journal ArticleDOI

A Singular Value Thresholding Algorithm for Matrix Completion

TL;DR: This paper develops a simple first-order and easy-to-implement algorithm that is extremely efficient at addressing problems in which the optimal solution has low rank, and develops a framework in which one can understand these algorithms in terms of well-known Lagrange multiplier algorithms.
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