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Journal ArticleDOI

Characterization and reduction of artifacts in limited angle tomography

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TLDR
In this article, the authors consider the reconstruction problem for limited angle tomography using filtered backprojection (FBP) and lambda tomography, and they use microlocal analysis to explain why the well-known streak artifacts are present at the end of the limited angular range.
Abstract
We consider the reconstruction problem for limited angle tomography using filtered backprojection (FBP) and lambda tomography. We use microlocal analysis to explain why the well-known streak artifacts are present at the end of the limited angular range. We explain how to mitigate the streaks and prove that our modified FBP and lambda operators are standard pseudodifferential operators, and so they do not add artifacts. We provide reconstructions to illustrate our mathematical results.

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Journal ArticleDOI

X-ray computed tomography of polymer composites

TL;DR: In this paper, the technical aspects relating to the X-ray CT imaging of composites such as obtaining sufficient contrast, examination of thin panels, sample size/resolution issues, quantification of damage and defects, and image-based modelling are reviewed.
Book ChapterDOI

Introduction to the Theory of Distributions

TL;DR: In this paper, the basic ideas of the theory of distributions are presented and a brief but solid introduction to the theory is given, particularly to those aspects that are important in the theory for asymptotic expansions.
Journal ArticleDOI

Joint image reconstruction and segmentation using the Potts model

TL;DR: A new algorithmic approach to the non-smooth and non-convex Potts problem (also called piecewise-constant Mumford-Shah problem) for inverse imaging problems is proposed and a suitable splitting into specific subproblems that can all be solved efficiently is derived.
Journal ArticleDOI

Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography

TL;DR: In this article, a hybrid reconstruction framework that fuses model-based sparse regularization with data-driven deep learning was developed for the inverse problem of limited angle computed tomography, in which entire boundary sections are not captured in the measurements.
Posted Content

Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network.

TL;DR: A data-driven learning-based method is proposed based on a deep convolutional neural network that provides a simple and efficient approach for improving image quality of the reconstruction results from limited projection data.
References
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Book

The Mathematics of Computerized Tomography

TL;DR: In this paper, the Radon transform and related transforms have been studied for stability, sampling, resolution, and accuracy, and quite a bit of attention is given to the derivation, analysis, and practical examination of reconstruction algorithm, for both standard problems and problems with incomplete data.
Journal Article

Clinical applications of cone-beam computed tomography in dental practice.

TL;DR: An overview of currently available maxillofacial CBCT systems is provided and the specific application of various CBCT display modes to clinical dental practice is reviewed.
Journal ArticleDOI

Reconstruction of Three Dimensional Structures from Electron Micrographs

TL;DR: General principles are formulated for the objective reconstruction of a three dimensional object from a set of electron microscope images and applied to the calculation of aThree dimensional density map of the tail of bacteriophage T4.
Journal ArticleDOI

Digital x-ray tomosynthesis: current state of the art and clinical potential

TL;DR: The various reconstruction algorithms used to produce tomosynthesis images, as well as approaches used to minimize the residual blur from out-of-plane structures are described.
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