Open AccessSynergistic tomographic image reconstruction: part 1
Author(s) -
Charalampos Tsoumpas,
Jakob Sauer Jørgensen,
Christoph Kolbitsch,
Kris Thielemans
Publication year - 2021
Publication title -
philosophical transactions - royal society. mathematical, physical and engineering sciences/philosophical transactions - royal society. mathematical, physical and engineering sciences
Language(s) - English
Resource type - Journals
eISSN - 1471-2962
pISSN - 1364-503X
DOI - 10.1098/rsta.2020.0189
Subject(s) - computer science , tomographic reconstruction , inverse problem , range (aeronautics) , decomposition , bayesian probability , image (mathematics) , monte carlo method , iterative reconstruction , data science , theme (computing) , artificial intelligence , machine learning , mathematics , statistics , mathematical analysis , ecology , materials science , composite material , biology , operating system
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’.