Journal article
Synergistic tomographic image reconstruction: part 1
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 |
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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 |
Computed tomography Electrical impedance tomography Imaging Magnetic resonance imaging Positron emission tomography SDG 3 - Good Health and Well-being Tomography
Algorithms Bayes Theorem Computer Simulation Humans Image Processing, Computer-Assisted Machine Learning Mathematical Concepts Monte Carlo Method Multimodal Imaging computed tomography electrical impedance tomography imaging magnetic resonance imaging positron emission tomography tomography