TY  - JOUR
AU  - Neishabouri, Ahmad
AU  - Bauer, Julia
AU  - Abdollahi, Amir
AU  - Debus, Jürgen
AU  - Mairani, Andrea
TI  - Real-time adaptive proton therapy: An AI-based spatio-temporal mono-energetic dose calculation model (CC-LSTM).
JO  - Computers in biology and medicine
VL  - 188
SN  - 0010-4825
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - DKFZ-2025-00371
SP  - 109777
PY  - 2025
N1  - #EA:E050#
AB  - To develop a fully AI-based dose estimation model capable of learning and estimating single pencil beam dose distributions, and to verify its performance by testing the model's generalizability on unseen, previously delivered treatment plans. Additionally, the model aims to achieve super-fast runtimes suitable for incorporation into real-time adaptive proton therapy (APT).A mono-energetic, end-to-end PB dose estimation task was defined using input Relative Stopping Power (RSP) and corresponding output dose distributions. A cohort of 90 Low-Grade-Glioma (LGG) patients was used for training and testing. The proposed CC-LSTM model employs 2-layer CNNs to extract spatial features from Beam's Eye View (BEV) slices, followed by a custom ConvLSTM to propagate 2D features along the beam path. A 3-layer CNN then reconstructs 2D dose distributions, which, in an auto-regressive scheme, form the 3D dose distribution of a single PB.CC-LSTM demonstrated notable accuracy improvements over the RNN-based model, with the average local gamma-index pass rate at [1
KW  - AI-based dose calculation (Other)
KW  - Adaptive proton therapy (Other)
KW  - Deep learning (Other)
KW  - Dose calculation (Other)
KW  - LSTM (Other)
KW  - Proton therapy (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:39946787
DO  - DOI:10.1016/j.compbiomed.2025.109777
UR  - https://inrepo02.dkfz.de/record/298937
ER  -