Title: GPU-accelerated FREDopt package for simultaneous dose and LETd proton radiotherapy plan optimization via superiorization methods Show Chinese title

Title: 基于GPU加速的FREDopt软件包，通过超优化方法实现质子放疗计划的剂量和LETd同时优化

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. Show Chinese abstract

Abstract: 本研究介绍了FREDopt，一种新开发的GPU加速的开源优化软件，用于质子治疗计划中同时优化剂量和剂量平均LET（LETd）。 FREDopt完全用Python实现，利用CuPy进行GPU加速，并集成了来自FRED代码的快速蒙特卡罗（MC）模拟。 治疗计划优化流程包括预优化和优化，后者配备了可行性寻找算法的新型优越化方法。 可行性寻找需要找到满足预定约束的点。 优越化将计算扰动交织到迭代的可行性寻找步骤中，以引导它们向更优的可行点发展，从而取代对昂贵的完整约束优化的需要。 该方法在两家临床质子治疗中心的两个治疗计划中进行了验证，在重新优化前后比较了剂量和LETd分布。 使用FREDopt进行同时剂量和LETd优化显著降低了器官风险区（OARs）中的LETd和（剂量）×（LETd），同时保持了靶区剂量的符合性。 计算性能评估显示执行时间为14-50分钟，具体取决于算法和靶区体积大小——对于临床和研究应用来说是令人满意的，同时促进了经过充分测试、文档齐全的开源软件的进一步开发。