TY - JOUR
T1 - Synergistic tomographic image reconstruction
T2 - Part 1
AU - Tsoumpas, Charalampos
AU - Jørgensen, Jakob Sauer
AU - Kolbitsch, Christoph
AU - Thielemans, Kris
N1 - Funding Information:
Data accessibility. This article has no additional data. Authors’ contributions. C.T. drafted the manuscript; J.S.J., C.K. and K.T. wrote sections and revised the manuscript; and all authors read and approved the manuscript. Competing interests. C.T. serves as an executive member of the advisory board of Positrigo AG, Zürich, Switzerland. K.T. is the Director of Algorithms and Software Consulting Ltd, London, UK. The other authors report no conflicts of interest. Funding. This work was funded by the UK EPSRC grants no. ‘Computational Collaborative Project in Synergistic PET/MR Reconstruction’ (CCP PETMR) EP/M022587/1 and its associated Software Flagship project EP/P022200/1; the ‘Computational Collaborative Project in Synergistic Reconstruction for Biomedical Imaging’ (CCP SyneRBI) EP/T026693/1; ‘A Reconstruction Toolkit for Multichannel CT’ EP/P02226X/1 and ‘Collaborative Computational Project in tomographic imaging’ (CCPi) EP/M022498/1 and EP/T026677/1. C.T. is sponsored by a Royal Society Industry Fellowship (IF170011). J.S.J. was partially supported by The Villum Foundation (grant no. 25893).
Publisher Copyright:
© 2021 The Authors.
PY - 2021/6/28
Y1 - 2021/6/28
N2 - 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'.
AB - 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'.
KW - computed tomography
KW - electrical impedance tomography
KW - imaging
KW - magnetic resonance imaging
KW - positron emission tomography
KW - tomography
U2 - 10.1098/rsta.2020.0189
DO - 10.1098/rsta.2020.0189
M3 - Article
C2 - 33966460
AN - SCOPUS:85105687694
SN - 1364-503X
VL - 379
JO - Philosophical transactions of the royal society a-Mathematical physical and engineering sciences
JF - Philosophical transactions of the royal society a-Mathematical physical and engineering sciences
IS - 2200
M1 - 20200189
ER -