A deep learning approach to radiation dose estimation.

Götz, Th I; Schmidkonz, C; Chen, S; Al-Baddai, S; Kuwert, T; Lang, E W

Götz, Th I; Schmidkonz, C; Chen, S; Al-Baddai, S; Kuwert, T; Lang, E W.

Affiliation

Götz TI; Clinic of Nuclear Medicine, University Hospital Erlangen, 91054 Erlangen, Germany. CIML Group, Biophysics, University of Regensburg, 93040 Regensburg, Germany. Pattern Recognition Lab, University of Erlangen-Nürnberg, 91058 Erlangen, Germany. Author to whom any correspondence may be addressed.

Phys Med Biol ; 65(3): 035007, 2020 02 04.

Article in En | MEDLINE | ID: mdl-31881547

Subject(s)

Algorithms; Deep Learning; Lutetium/pharmacokinetics; Lutetium/therapeutic use; Monte Carlo Method; Neoplasms/radiotherapy; Organs at Risk/radiation effects; Radioisotopes/pharmacokinetics; Radioisotopes/therapeutic use; Glutamate Carboxypeptidase II/metabolism; Humans; Neoplasms/metabolism; Neural Networks, Computer; Radiation Dosage; Radiopharmaceuticals/therapeutic use; Reproducibility of Results; Tissue Distribution; Tomography, X-Ray Computed/methods

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Radioisotopes / Algorithms / Monte Carlo Method / Organs at Risk / Deep Learning / Lutetium / Neoplasms Type of study: Etiology_studies / Guideline / Health_economic_evaluation / Prognostic_studies Limits: Humans Language: En Journal: Phys Med Biol Year: 2020 Type: Article

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