Synergistic tomographic image reconstruction: Part 1

Charalampos Tsoumpas*, Jakob Sauer Jørgensen, Christoph Kolbitsch, Kris Thielemans

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
25 Downloads (Pure)


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'.

Original languageEnglish
Article number20200189
Number of pages5
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2200
Publication statusPublished - 28-Jun-2021
Externally publishedYes


  • computed tomography
  • electrical impedance tomography
  • imaging
  • magnetic resonance imaging
  • positron emission tomography
  • tomography


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