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Potential of Deep Learning in Quantitative Magnetic Resonance Imaging for Personalized Radiotherapy.
Gurney-Champion, Oliver J; Landry, Guillaume; Redalen, Kathrine Røe; Thorwarth, Daniela.
Affiliation
  • Gurney-Champion OJ; Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
  • Landry G; Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Munich, Germany.
  • Redalen KR; Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway.
  • Thorwarth D; Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), Partner site Tübingen, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address: daniela.thorwarth@med.uni-tuebingen.de.
Semin Radiat Oncol ; 32(4): 377-388, 2022 10.
Article in En | MEDLINE | ID: mdl-36202440
Quantitative magnetic resonance imaging (qMRI) has been shown to provide many potential advantages for personalized adaptive radiotherapy (RT). Deep learning models have proven to increase efficiency, robustness and speed for different qMRI tasks. Therefore, this article discusses the current state-of-the-art and potential future opportunities as well as challenges related to the use of deep learning in qMRI for target contouring, quantitative parameter estimation and also the generation of synthetic computerized tomography (CT) data based on MRI in personalized RT.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Learning Limits: Humans Language: En Journal: Semin Radiat Oncol Journal subject: NEOPLASIAS / RADIOLOGIA Year: 2022 Document type: Article Affiliation country: Netherlands Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Learning Limits: Humans Language: En Journal: Semin Radiat Oncol Journal subject: NEOPLASIAS / RADIOLOGIA Year: 2022 Document type: Article Affiliation country: Netherlands Country of publication: United States