An Automatic Regularization Method : An Application for 3-D X-Ray Micro-CT Reconstruction Using Sparse Data

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Purisha , Z , Karhula , S S , Ketola , J H , Rimpeläinen , J , Nieminen , M T , Saarakkala , S , Kröger , H & Siltanen , S 2019 , ' An Automatic Regularization Method : An Application for 3-D X-Ray Micro-CT Reconstruction Using Sparse Data ' , IEEE Transactions on Medical Imaging , vol. 38 , no. 2 , pp. 417-425 . https://doi.org/10.1109/TMI.2018.2865646

Title: An Automatic Regularization Method : An Application for 3-D X-Ray Micro-CT Reconstruction Using Sparse Data
Author: Purisha, Zenith; Karhula, Sakari S.; Ketola, Juuso H.; Rimpeläinen, Juho; Nieminen, Miika T.; Saarakkala, Simo; Kröger, Heikki; Siltanen, Samuli
Contributor: University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, Department of Mathematics and Statistics
Date: 2019-02
Language: eng
Number of pages: 9
Belongs to series: IEEE Transactions on Medical Imaging
ISSN: 0278-0062
URI: http://hdl.handle.net/10138/307032
Abstract: X-ray tomography is a reliable tool for determining the inner structure of 3-D object with penetrating X-rays. However, traditional reconstruction methods, such as Feldkamp-Davis-Kress (FDK), require dense angular sampling in the data acquisition phase leading to long measurement times, especially in X-ray micro-tomography to obtain high-resolution scans. Acquiring less data using greater angular steps is an obvious way for speeding up the process and avoiding the need to save huge data sets. However, computing 3-D reconstruction from such a sparsely sampled data set is difficult because the measurement data are usually contaminated by errors, and linear measurement models do not contain sufficient information to solve the problem in practice. An automatic regularization method is proposed for robust reconstruction, based on enforcing sparsity in the 3-D shearlet transform domain. The inputs of the algorithm are the projection data and a priori known expected degree of sparsity, denoted as 0 <C-pr
Subject: Biomedical imaging
sparse X-ray tomography
iterative methods
bone
shearlets
ASTRA TOOLBOX
BONE
TOMOGRAPHY
RADIATION
THICKNESS
ALGORITHM
111 Mathematics
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