DOI : 10.1055/s-00034924

Nuklearmedizin - NuclearMedicine

Issue 02 · Volume 60 · April 2021 DOI: 10.1055/s-011-50684

Deutsche Gesellschaft für Nuklearmedizin
NuklearMedizin 2021 – digital
, 14.-17.04.2021

Prof. Dr. Michael Schäfers
Abstracts (HTML) Abstracts (PDF) List of Authors
V69
Solari, EL; Gafita, A; Schachoff, S; Visvikis, D; Weber, W; Eiber, M; Hatt, M; Nekolla, SG: PSMA PET/MR radiomics to improve postsurgical Gleason score prediction in prostate cancer
V70
Hardiansyah, D; Riana, A; Kletting, P; Zaid, N; Beer, AJ; Glatting, G: Population-Based Model Selection Algorithm developed at the example of Lu-177-PSMA Therapy
V71
Guerra, JM; Mustafa, M; Pandeva, T; Pinto, FA; Matthies, P; Brosch-Lenz, J; Stollenga, M; Perret, O; Ladikos, A; Ziegler, S; Todica, A; Böning, G; Navab, N; Nekolla, SG; Weber, WA; Wendler, T: Performance of automatic Liver Volumetry for Selective Internal Radiotherapy
V72
Lohmann, P; Meissner, A; Werner, J; Stoffels, G; Kocher, M; Bauer, EK; Fink, GR; Shah, NJ; Langen, K; Galldiks, N: Hybrid FET PET/MRI radiomics for the non-invasive prediction of the MGMT promoter methylation in brain tumors
V73
Kotulski, B; Holzgreve, A; Brosch-Lenz, J; Gosewisch, A; Böning, G; Bartenstein, P; Albert, NL; Ziegler, S; Kaiser, L: Abhängigkeit der Intensitätsgrenzwerte zur optimalen F-18-FET-PET-basierten Gliom-Segmentierung von Tumorform und Intensitätsparametern
V74
Kersting, D; Weber, M; Umutlu, L; Schäfers, M; Rischpler, C; Fendler, WP; Buvat, I; Herrmann, K; Seifert, R: Using a Lymphoma and Lung Cancer Trained Neural Network to Predict the Outcome for Breast Cancer on FDG PET/CT Data
V75
Hong, J; Shi, K; Rominger, A; Choi, H: Spatial distribution pattern of tau depositions in Alzheimer’s disease using data-driven approach of flortaucipir PET
V76
Zaid, N; Kletting, P; Winter, G; Beer, AJ; Glatting, G: A whole-body physiologically-based pharmacokinetic model for alpha particle emitting bismuth in rats
V77
Blum, D; Hepp, T; Belov, V; Goya-Maldonaldo, R; la Fougère, C; Reimold, M: Controls-based denoising (CODE): Evaluation anhand von T1-MRT von 3058 Patienten und Probanden und Erweiterung der Methode (gewichtete Muster nicht-pathologischer Varianz mit analytischer, regularisierbarer Lösung für die Optimalgewichte)
V78
Christoph, C; Birindelli, G; Pizzichemi, M; Kruithof-de Julio, M; Auffray, E; Rominger, A; Shi, K: Reconstruct gamma-ray interaction position for the development of an on-chip PET system using deep learning