Abstract
Accurate Monte Carlo (MC) dose calculation is essential in radiotherapy, but obtaining high-sampling (HS) dose maps requires a very large number of particle histories and remains computationally expensive. Deep learning has recently been investigated as an asymptotic variance reduction strategy to reconstruct HS dose maps from low-sampling (LS) simulations. However, most existing approaches formulate this task as deterministic image-to-image regression and may be sensitive to anatomical distribution shifts. This limitation is particularly important when HS references are scarce for a new anatomical site. In this work, we investigate conditional denoising diffusion probabilistic models (DDPMs) for MC dose reconstruction beyond the training anatomy. The proposed model learns the conditional distribution of the HS dose map given the corresponding LS simulation and planning computed tomography (CT) image. It reconstructs the HS dose map through an iterative stochastic denoising process rather than a single deterministic prediction. The model is trained on abdominal data and evaluated on abdominal test data and five unseen pelvic and head-and-neck slices. On the abdominal test set, it performs close to a deterministic U-Net. On the limited cross-anatomy set, the DDPM produces visually more faithful reconstructions in the examples considered. Geometric alignment further improves its results. Although preliminary, these findings motivate larger-scale evaluation of conditional diffusion models for MC dose reconstruction.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MIART_020.pdf
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SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{DanHon_Generalizing_MICCAISAT2026,
author = { Dang, Hong-Phuong AND Gozan, Richard},
title = { { Generalizing Monte Carlo dose reconstruction beyond the training anatomy using conditional diffusion models } },
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}
}