%0 Journal Article
%A Xiao, Fan
%A Radonic, Domagoj
%A Kriechbaum, Michael
%A Wahl, Niklas
%A Neishabouri, Ahmad
%A Delopoulos, Nikolaos
%A Parodi, Katia
%A Corradini, Stefanie
%A Belka, Claus
%A Kurz, Christopher
%A Landry, Guillaume
%A Dedes, Georgios
%T Prompt gamma emission prediction using a long short-term memory network.
%J Physics in medicine and biology
%V 69
%N 23
%@ 0031-9155
%C Bristol
%I IOP Publ.
%M DKFZ-2024-02228
%P 235003
%D 2024
%Z  Physics in Medicine & Biology, Volume 69, Number 23 , 2024 Phys. Med. Biol. 235003
%X To present a long short-term memory (LSTM)-based prompt gamma (PG) emission prediction method for proton therapy.Computed tomography (CT) scans of 33 patients with a prostate tumor were included in the dataset. A set of 10 million histories proton pencil beam (PB)s was generated for Monte Carlo (MC) dose and PG simulation. For training (20 patients) and validation (3 patients), over 6000 PBs at 150, 175 and 200 MeV were simulated. 3D relative stopping power (RSP), PG and dose cuboids that included the PB were extracted. Three models were trained, validated and tested based on an LSTM-based network: (1) input RSP and output PG, (2) input RSP with dose and output PG (single-energy), and (3) input RSP/dose and output PG (multi-energy). 540 PBs at each of the four energy levels (150, 175, 200, and 125-210 MeV) were simulated across 10 patients to test the three models. The gamma passing rate (2
%K LSTM (Other)
%K deep learning (Other)
%K prompt gamma (Other)
%K proton therapy (Other)
%K range verification (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:39488071
%R 10.1088/1361-6560/ad8e2a
%U https://inrepo02.dkfz.de/record/294399