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Distortion-corrected image reconstruction with deep learning on an MRI-Linac.
Shan, Shanshan; Gao, Yang; Liu, Paul Z Y; Whelan, Brendan; Sun, Hongfu; Dong, Bin; Liu, Feng; Waddington, David E J.
Affiliation
  • Shan S; ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
  • Gao Y; State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, Jiangsu, China.
  • Liu PZY; Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia.
  • Whelan B; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.
  • Sun H; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.
  • Dong B; School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.
  • Liu F; ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
  • Waddington DEJ; Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia.
Magn Reson Med ; 90(3): 963-977, 2023 09.
Article de En | MEDLINE | ID: mdl-37125656
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Radiothérapie guidée par l'image / Apprentissage profond Type d'étude: Guideline Limites: Humans Langue: En Journal: Magn Reson Med Sujet du journal: DIAGNOSTICO POR IMAGEM Année: 2023 Type de document: Article Pays d'affiliation: Australie
Texte intégral
Ajouter à My VHL
Imprimer
XML
PubMed Links
Recherche sur Google

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Radiothérapie guidée par l'image / Apprentissage profond Type d'étude: Guideline Limites: Humans Langue: En Journal: Magn Reson Med Sujet du journal: DIAGNOSTICO POR IMAGEM Année: 2023 Type de document: Article Pays d'affiliation: Australie