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Using deep learning to generate synthetic CTs for radiotherapy treatment planning
Bylund, Mikael
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
ORCID iD:
0000-0001-7539-2262
Jonsson, Joakim
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Lundman, Josef
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Brynolfsson, Patrik
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Garpebring, Anders
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
ORCID iD:
0000-0002-0532-232X
Nyholm, Tufve
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
ORCID iD:
0000-0002-8971-9788
Löfstedt, Tommy
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Show others and affiliations
2018 (English)
In:
Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 127, p. S283-S283
Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 127, p. S283-S283
National Category
Cancer and Oncology
Identifiers
URN:
urn:nbn:se:umu:diva-150491
DOI:
10.1016/S0167-8140(18)30842-9
ISI:
000437723401144
OAI: oai:DiVA.org:umu-150491
DiVA, id:
diva2:1260243
Conference
37th Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO), APR 20-24, 2018, Barcelona, SPAIN
Available from:
2018-11-01
Created:
2018-11-01
Last updated:
2024-07-02
Bibliographically approved
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Bylund, Mikael
Jonsson, Joakim
Lundman, Josef
Brynolfsson, Patrik
Garpebring, Anders
Nyholm, Tufve
Löfstedt, Tommy
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Bylund, Mikael
Jonsson, Joakim
Lundman, Josef
Brynolfsson, Patrik
Garpebring, Anders
Nyholm, Tufve
Löfstedt, Tommy
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Radiation Physics
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Radiotherapy and Oncology
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Cancer and Oncology
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