TY  - JOUR
AU  - Neishabouri, Ahmad
AU  - Wahl, Niklas
AU  - Mairani, Andrea
AU  - Köthe, Ullrich
AU  - Bangert, Mark
TI  - Long short-term memory networks for proton dose calculation in highly heterogeneous tissues.
JO  - Medical physics
VL  - 48
IS  - 4
SN  - 2473-4209
CY  - College Park, Md.
PB  - AAPM
M1  - DKFZ-2020-02941
SP  - 1893-1908
PY  - 2021
N1  - #EA:E040#LA:E040# / 2021 Apr;48(4):1893-1908
AB  - To investigate the feasibility and accuracy of proton dose calculations with artificial neural networks (ANN) in challenging 3D anatomies.A novel proton dose calculation approach was designed based on the application of a long short-term memory (LSTM) network. It processes the 3D geometry as a sequence of two-dimensional (2D) computed tomography slices and outputs a corresponding sequence of (2D) slices that forms the 3D dose distribution. The general accuracy of the approach is investigated in comparison to Monte Carlo reference simulations and pencil beam dose calculations. We consider both artificial phantom geometries and clinically realistic lung cases for three different pencil beam energies.For artificial phantom cases, the trained LSTM model achieved a 98:57
LB  - PUB:(DE-HGF)16
C6  - pmid:33332644
DO  - DOI:10.1002/mp.14658
UR  - https://inrepo02.dkfz.de/record/166498
ER  -