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Solutions for MR-based RT planning with an open low field scanner using neural network tools
Fetty, L.
Buschmann, M.
Heilemann, G.
Kuess, P.
Nyholm, Tufve
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
ORCID iD:
0000-0002-8971-9788
Herrmann, H.
Georg, D.
Nesvacil, N.
Show others and affiliations
2019 (English)
In:
Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 133, p. S567-S568
Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 133, p. S567-S568
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN:
urn:nbn:se:umu:diva-160315
DOI:
10.1016/S0167-8140(19)31444-6
ISI:
000468315602288
OAI: oai:DiVA.org:umu-160315
DiVA, id:
diva2:1325752
Conference
38th Annual Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO), Milan, ITALY, April 26-30, 2019
Available from:
2019-06-17
Created:
2019-06-17
Last updated:
2024-07-02
Bibliographically approved
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(711 kB)
245 downloads
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3510ff2034ecffe8bbe66206163314b34c4e004743ba05d0428594cfcfa4cd25e4547a378c1e640d2a2d79a1acf45c40af798aa1854bb849930a0bd15860a81a
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Nyholm, Tufve
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Radiation Physics
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Permanent link
https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160315
Direct link
https://umu.diva-portal.org/smash/record.jsf?pid=diva2:1325752
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