GPU-accelerated FREDopt package for simultaneous dose and LETd proton radiotherapy plan optimization via superiorization methods
Abstract
This study presents FREDopt, a newly developed GPU-accelerated open-source optimization software for simultaneous proton dose and dose-averaged LET (LETd) optimization in IMPT treatment planning. FREDopt was implemented entirely in Python, leveraging CuPy for GPU acceleration and incorporating fast Monte Carlo (MC) simulations from the FRED code. The treatment plan optimization workflow includes pre-optimization and optimization, the latter equipped with a novel superiorization of feasibility-seeking algorithms. Feasibility-seeking requires finding a point that satisfies prescribed constraints. Superiorization interlaces computational perturbations into iterative feasibility-seeking steps to steer them toward a superior feasible point, replacing the need for costly full-fledged constrained optimization. The method was validated on two treatment plans of patients treated in a clinical proton therapy center, with dose and LETd distributions compared before and after reoptimization. Simultaneous dose and LETd optimization using FREDopt led to a substantial reduction of LETd and (dose)x(LETd) in organs at risk (OARs) while preserving target dose conformity. Computational performance evaluation showed execution times of 14-50 minutes, depending on the algorithm and target volume size-satisfactory for clinical and research applications while enabling further development of the well-tested, documented open-source software.
Cite
@article{arxiv.2509.20012,
title = {GPU-accelerated FREDopt package for simultaneous dose and LETd proton radiotherapy plan optimization via superiorization methods},
author = {Damian Borys and Jan Gajewski and Tobias Becher and Yair Censor and Renata Kopeć and Marzena Rydygier and Angelo Schiavi and Tomasz Skóra and Anna Spaleniak and Niklas Wahl and Agnieszka Wochnik and Antoni Ruciński},
journal= {arXiv preprint arXiv:2509.20012},
year = {2025}
}
Comments
29 pages. Open Access at: https://iopscience.iop.org/article/10.1088/1361-6560/ade841