Abstract
Deep learning (DL) enables fast 3D dose calculation for radiotherapy, but existing approaches are restricted to a single treatment technique or tumor site. Furthermore, representing delivery parameters in the patient domain remains challenging. We propose a generalizable DL framework for full-plan dose calculation that reconstructs the full beam path by projecting the fluence maps into the patient domain and that incorporates PTV information to guide target dose prediction. A heterogeneous model trained jointly on lung IMRT and head-and-neck (H&N) VMAT was compared with modality-specific models to evaluate generalization across treatment techniques and tumor sites. Compared with homogeneous training, heterogeneous training increased the average gamma passing rate (98.42% vs. 98.26% for H&N VMAT; 98.60% vs. 98.54% for lung IMRT) and reduced mean dose difference for the planning target volume (2.07% vs. 3.34% for H&N VMAT; 2.16% vs. 2.82% for lung IMRT). Organs at risk mean dose differences remained below 3% for both approaches. Ablation experiments demonstrated that beam-path reconstruction and PTV information improve prediction accuracy.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MIART_012.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{VanLoe_Heterogeneous_MICCAISAT2026,
author = { Vandenbroucke, Loes AND Van Riet, Emma AND Callens, Dylan AND Berkovic, Patrick AND Lambrecht, Maarten AND Nuyts, Sandra AND Crijns, Wouter AND Maes, Frederik},
title = { { Heterogeneous IMRT and VMAT Dose Calculation using Deep Learning with Beam Path Reconstruction and PTV Information } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17274},
month = {pending},
page = {pending}
}