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| Home > Publications database > Prompt gamma emission prediction using a long short-term memory network. |
| Journal Article | DKFZ-2024-02228 |
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2024
IOP Publ.
Bristol
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Please use a persistent id in citations: doi:10.1088/1361-6560/ad8e2a
Abstract: 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%/2mm) and PG range shift were evaluated and compared among the three models.The model with input RSP/dose and output PG (multi-energy) showed the best performance in terms of gamma passing rate and range shift metrics. Its mean gamma passing rate of testing PBs of 125-210 MeV was 98.5% and the worst case was 92.8%. Its mean absolute range shift between predicted and MC PGs was 0.15 mm, where the maximum shift was 1.1mm. The prediction time of our models was within 130 ms per PB.We developed a sub-second LSTM-based PG emission prediction method. Its accuracy in prostate patients has been confirmed across an extensive range of proton energies.
Keyword(s): LSTM ; deep learning ; prompt gamma ; proton therapy ; range verification
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