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optiGAN:基于Python的GATE (10+) 中基于深度学习的替代光学光子跟踪

Guneet Mummaneni¹, Carlotta Trigila², Nils Krah^3,4

¹Department of Computer Science, University of California, Davis, Davis, CA, United States of America.

Physics in medicine and biology

|June 9, 2025

概括

这项研究将optiGAN,一种生成对抗网络 (GAN) 集成到GATE 10中,以实现更快的光学光子传输模拟. 新方法的准确度超过92%,并将模拟时间缩短约50%.

科学领域:

医学物理医学物理
计算科学计算科学
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

光学蒙特卡洛方法对于光子传输模拟是准确的,但在计算上昂贵.
加快这些模拟对于推进医学成像和探测器设计至关重要.
盖特模拟框架是医学物理研究中的一个关键工具.

研究的目的:

在GATE医学物理框架中加速光学光子传输模拟.
将一个名为optiGAN的生成对抗网络 (GAN) 集成到新的基于Python的GATE 10.
为了确保高的建模准确性,同时降低计算成本.

主要方法:

集成optiGAN,一个GAN模型,进入GATE 10.
根据GATE v9.3.3对GATE 10光学光子传输模块进行了验证.
将GATE 10中的完整蒙特卡洛模拟与GATE 10-optiGAN模拟进行了比较.

主要成果:

GATE 10的结果与GATE v9.3.3的结果一致.
门10-optiGAN模拟显示超过92%的相似性与蒙特卡洛的结果.
使用GATE 10-optiGAN,模拟时间缩短了约50%.

结论:

在GATE 10中确认了光学光子传输建模的真实性.
通过optiGAN. ,通过深度学习实现了有效的加速.
实现了大规模,高保真度的光学模拟,降低了医学成像计算成本.

关键词:

门10 门10 是一个门.蒙特卡洛模拟的蒙特卡洛模拟深度学习加速加速生成性的对抗性网络.多维分布的多维分布.光学光子传输的方法

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