使用LSTM网络在磁场存在的情况下计算质子剂量
Domagoj Radonic1,2, Fan Xiao1, Niklas Wahl3,4
1Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
Physics in medicine and biology
|September 24, 2024
概括
研究人员开发了一个长期短期记忆 (LSTM) 网络,用于在磁共振引导的质子疗法中准确计算质子剂量. 这种人工智能方法显示了前列腺癌治疗计划的高精度和效率.
科学领域:
- 医学物理 医学物理
- 在放射治疗中使用人工智能
- 计算剂量计计算剂量计.
背景情况:
- 磁共振 (MR) 引导的质子疗法提供了先进的精度,但需要在磁场中精确的剂量计算.
- 传统的剂量计算方法可能是计算密集的,限制实时应用.
- 开发高效准确的剂量计算算法对于MR引导的质子疗法至关重要.
研究的目的:
- 引入一种新的长短期记忆 (LSTM) 基于网络的方法来计算质子剂量.
- 评估LSTM模型在1.5T磁场中的准确性和效率,用于前列腺癌患者.
- 将LSTM模型性能与传统的蒙特卡洛模拟进行比较.
主要方法:
- 使用蒙特卡洛 (MC) 方法模拟了35名前列腺癌患者在1.5T磁场下的质子笔束 (PB) 剂量.
- 提取了3D相对制动功率数据,并用于训练单能 (SE) 和多能 (ME) LSTM模型.
- 一个连续能量 (CE) LSTM模型在广泛的能量范围 (125-200 MeV) 上受过训练,并用于治疗计划的重新计算.
主要成果:
- 在SE LSTM模型中,达到了>99.9%的马通道率和<0.4mm的质量中心差异.
- ME LSTM 模型显示了>99.8%的马通过率和<0.5mm的COM差异.
- 在治疗计划重新计算中,CE模型实现了高马合格率 (99.6%,97.9%) 与快速推断时间 (9-10毫秒/PB).
结论:
- 在MR引导的质子疗法中,LSTM网络为质子剂量计算提供了高度准确和高效的解决方案.
- 开发的模型显示出在前列腺癌治疗中临床实施的重大前景.
- 这种人工智能驱动的方法可以加快剂量计算,潜在地改善治疗交付和患者的结果.
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