医学物理

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[1] arXiv:2606.27086 (替换) [中文pdf, pdf, html, 其他]

标题： 带有 SupPy 的约束物理问题的 GPU 加速超定化 显示英文标题

标题： GPU-accelerated superiorization on constrained physical problems with SupPy

Tobias Becher, Yair Censor, Kay Barshad, Niklas Wahl

主题： 计算物理 (physics.comp-ph) ; 医学物理 (physics.med-ph)

优于化方法（SM）位于可行性寻求和约束优化之间。 而不是在约束集上追求给定目标函数的最小值，它寻求一个可行点，该点的目标函数值被降低——尽管不一定是最小的——而不是硬性目标，或者在数学上最优解不需要严格满足的情况下。 虽然该方法已在物理学的几个应用中进行了研究，但由于研究人员希望探索它时缺乏公开可用的软件，其更广泛的应用受到了限制。 在本工作中，我们将优于化应用于应用物理中的三个问题：地震图像重建、低剂量CT重建和调强放疗治疗计划。 这些实验是使用SupPy进行的，SupPy是一个为本工作开发的开源模块化Python工具箱，支持在CPU和GPU上执行可行性寻求算法及其优于化版本。 在这三种情况下，优于化算法与仅依靠可行性寻求相比取得了有利的结果，在成像例子中减少了噪声，在放疗计划中降低了身体剂量。 对于放疗情况，我们进一步观察到，优于化在不可行的约束集上产生了临床上可行的计划。 显示英文摘要

The superiorization method (SM) is situated between feasibility-seeking and constrained optimization. Instead of aiming at the minimum of a given objective function over a constraint set, it seeks a feasible point at which the objective function value is reduced - though not necessarily minimal - rather than hard targets, or in which a mathematically optimal solution is not strictly required. While the method has been investigated for several applications in physics, its broader use has been limited, in part due to the lack of openly available software for researchers wishing to explore it. In this work we apply superiorization to three problems from applied physics: seismic image reconstruction, low-dose CT reconstruction and intensity-modulated radiotherapy treatment planning. These experiments are conducted with SupPy, an open-source modularized Python toolbox developed for this work, which supports execution of feasibility-seeking algorithms and their superiorized version on both the CPU and the GPU. In all three cases the superiorized algorithms achieve favorable results compared to feasibility-seeking alone, with reduced noise in the imaging examples and lowered body dose in the radiotherapy plans. For the radiotherapy case we further observe that superiorization produces clinically viable plans on infeasible constraint sets.