JOINT RECONSTRUCTION IN LOW DOSE MULTI-ENERGY CT

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http://hdl.handle.net/10138/332723

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Toivanen , J , Meaney , A , Siltanen , S & Kolehmainen , V 2020 , ' JOINT RECONSTRUCTION IN LOW DOSE MULTI-ENERGY CT ' , Inverse problems and imaging , vol. 14 , no. 4 , pp. 607-629 . https://doi.org/10.3934/ipi.2020028

Title: JOINT RECONSTRUCTION IN LOW DOSE MULTI-ENERGY CT
Author: Toivanen, Jussi; Meaney, Alexander; Siltanen, Samuli; Kolehmainen, Ville
Contributor: University of Helsinki, University of Eastern Finland
University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, Department of Mathematics and Statistics
Date: 2020-08
Language: eng
Number of pages: 23
Belongs to series: Inverse problems and imaging
ISSN: 1930-8337
URI: http://hdl.handle.net/10138/332723
Abstract: Multi-energy CT takes advantage of the non-linearly varying attenuation properties of elemental media with respect to energy, enabling more precise material identification than single-energy CT. The increased precision comes with the cost of a higher radiation dose. A straightforward way to lower the dose is to reduce the number of projections per energy, but this makes tomographic reconstruction more ill-posed. In this paper, we propose how this problem can be overcome with a combination of a regularization method that promotes structural similarity between images at different energies and a suitably selected low-dose data acquisition protocol using non-overlapping projections. The performance of various joint regularization models is assessed with both simulated and experimental data, using the novel low-dose data acquisition protocol. Three of the models are well-established, namely the joint total variation, the linear parallel level sets and the spectral smoothness promoting regularization models. Furthermore, one new joint regularization model is introduced for multi-energy CT: a regularization based on the structure function from the structural similarity index. The findings show that joint regularization outperforms individual channel-by-channel reconstruction. Furthermore, the proposed combination of joint reconstruction and non-overlapping projection geometry enables significant reduction of radiation dose.
Subject: Computed tomography
iterative reconstruction
multi-energy CT
inverse problem
regularization
structural prior
DUAL-ENERGY CT
X-RAY TOMOGRAPHY
COMPUTED-TOMOGRAPHY
IMAGE-RECONSTRUCTION
STATISTICAL INVERSION
OPTIMIZATION
ALGORITHMS
TOMOSYNTHESIS
RADIOGRAPHS
111 Mathematics
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