TY  - JOUR
AU  - Radonic, Domagoj
AU  - Xiao, Fan
AU  - Wahl, Niklas
AU  - Voss, Luke
AU  - Neishabouri, Ahmad
AU  - Delopoulos, Nikolaos
AU  - Marschner, Sebastian
AU  - Corradini, Stefanie
AU  - Belka, Claus
AU  - Dedes, Georgios
AU  - Kurz, Christopher
AU  - Landry, Guillaume
TI  - Proton dose calculation with LSTM networks in presence of a magnetic field.
JO  - Physics in medicine and biology
VL  - 69
SN  - 0031-9155
CY  - Bristol
PB  - IOP Publ.
M1  - DKFZ-2024-01915
SP  - 215019
PY  - 2024
N1  - Med. Biol. 69, 215019
AB  - To present a long short-term memory (LSTM) network-based dose calculation method for magnetic resonance (MR)-guided proton therapy.35 planning computed tomography (CT) images of prostate cancer patients were collected for Monte Carlo (MC) dose calculation under a perpendicular 1.5 T magnetic field. Proton pencil beams (PB) at three energies (150, 175, and 200 MeV) were simulated (7560 PBs at each energy). A 3D relative stopping power (RSP) cuboid covering the extent of the PB dose was extracted and given as input to the LSTM model, yielding a 3D predicted PB dose. Three single-energy (SE) LSTM models were trained separately on the corresponding 150/175/200 MeV datasets and a multi-energy (ME) LSTM model with an energy embedding layer was trained on either the combined dataset with three energies or a continuous energy (CE) dataset with 1 MeV steps ranging from 125 to 200 MeV. For each model, training and validation involved 25 patients and 10 patients were for testing. Two single field uniform dose prostate treatment plans were optimized and recalculated with MC and the CE model.Test results of all PBs from the three SE models showed a mean gamma passing rate (2
KW  - LSTM (Other)
KW  - MR-guided proton therapy (Other)
KW  - deep learning (Other)
KW  - dose calculation (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:39317232
DO  - DOI:10.1088/1361-6560/ad7f1e
UR  - https://inrepo02.dkfz.de/record/293598
ER  -