Optimization of Deep Learning Model for Denoising Dose Profiles in Proton Therapy
Abstract
Proton therapy offers a dosimetric advantage over conventional radiotherapy through the Bragg peak, enabling precise dose delivery to the tumour while sparing surrounding tissue. Monte Carlo (MC) simulation provides the most accurate dose calculation but is computationally expensive for clinical workflows. A promising compromise is to simulate with a reduced number of primary histories and recover a high-quality dose distribution through a deep learning denoising step. In this work, we take a 3D multiscale U-Net from recent literature as a case study and investigate two improvements critical for clinical deployment. First, we replace the standard MSE training objective with a differentiable γ-index-based loss, extended to native 3D operation, and demonstrate that directly optimising the clinical evaluation metric yields a mean Gamma Passing Rate of 99.47\% at 2mm/2\% and 95.67\% at 1mm/1\% on the test set, compared to 92.10\% and 71.13\% for MSE. Second, we apply L1-norm structured filter pruning to reduce the model's memory footprint, achieving a 13.3\% parameter reduction with negligible performance loss under partial pruning, and 20\% reduction under full-block pruning after fine-tuning.
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BibTeX
@misc{jawaid2026optimization,
title = {{Optimization of Deep Learning Model for Denoising Dose Profiles in Proton Therapy}},
author = {Junaid Jawaid and Matteo Spezialetti and Gabriele Libardi and Giovanni Stilo and Filippo Mignosi},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.08843},
eprint = {2610.08843},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.08843},
}
RIS
TY - UNPB
TI - Optimization of Deep Learning Model for Denoising Dose Profiles in Proton Therapy
AU - Junaid Jawaid
AU - Matteo Spezialetti
AU - Gabriele Libardi
AU - Giovanni Stilo
AU - Filippo Mignosi
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.08843
UR - https://arxiv.org/abs/2610.08843
AB - Proton therapy offers a dosimetric advantage over conventional radiotherapy through the Bragg peak, enabling precise dose delivery to the tumour while sparing surrounding tissue. Monte Carlo (MC) simulation provides the most accurate dose calculation but is computationally expensive for clinical workflows. A promising compromise is to simulate with a reduced number of primary histories and recover a high-quality dose distribution through a deep learning denoising step. In this work, we take a 3D multiscale U-Net from recent literature as a case study and investigate two improvements critical for clinical deployment. First, we replace the standard MSE training objective with a differentiable γ-index-based loss, extended to native 3D operation, and demonstrate that directly optimising the clinical evaluation metric yields a mean Gamma Passing Rate of 99.47\% at 2mm/2\% and 95.67\% at 1mm/1\% on the test set, compared to 92.10\% and 71.13\% for MSE. Second, we apply L1-norm structured filter pruning to reduce the model's memory footprint, achieving a 13.3\% parameter reduction with negligible performance loss under partial pruning, and 20\% reduction under full-block pruning after fine-tuning.
ER -