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Journal article

Synergistic tomographic image reconstruction: part 1

From

University of Leeds1

Department of Applied Mathematics and Computer Science, Technical University of Denmark2

Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark3

Physikalisch-Technische Bundesanstalt4

University College London5

This special issue focuses on synergistic tomographic image reconstruction in a range of contributions in multiple disciplines and various application areas. The topic of image reconstruction covers substantial inverse problems (Mathematics) which are tackled with various methods including statistical approaches (e.g.

Bayesian methods, Monte Carlo) and computational approaches (e.g. machine learning, computational modelling, simulations). The issue is separated in two volumes. This volume focuses mainly on algorithms and methods. Some of the articles will demonstrate their utility on real-world challenges, either medical applications (e.g. cardiovascular diseases, proton therapy planning) or applications in material sciences (e.g. material decomposition and characterization).

One of the desired outcomes of the special issue is to bring together different scientific communities which do not usually interact as they do not share the same platforms (such as journals and conferences). This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

Language: English
Publisher: The Royal Society Publishing
Year: 2021
Pages: 20200189
ISSN: 14712962 and 1364503x
Types: Journal article
DOI: 10.1098/rsta.2020.0189
ORCIDs: 0000-0002-4971-2477 , Jørgensen, Jakob Sauer , 0000-0002-4355-8368 and 0000-0002-5514-199X

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