使用变压器计算质子剂量:将点地图转换为剂量
Xueyan Tang1, Hok Wan Chan Tseung1, Mark D Pepin1
1Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA.
Medical physics
|March 29, 2025
概括
这项研究引入了一种深度学习模型,用于快速准确的质子疗法剂量计算,实现近蒙特卡洛精度,比传统方法快得多. 该模型可以在治疗场所进行概括,提高辐射瘤学的效率.
科学领域:
- 医学物理 医学物理
- 计算生物学 计算生物学
- 辐射疗法 辐射疗法
背景情况:
- 传统的质子剂量计算方法在计算效率和准确性之间存在权衡.
- 蒙特卡洛 (MC) 模拟准确但耗时,而分析方法更快但不那么精确.
- 在质子疗法中,临床需要改进剂量计算方法.
研究的目的:
- 开发一种深度学习模型,用于计算在质子疗法中的剂量与水 (DW) 和剂量与介质 (DM) 的关系.
- 为了达到与MC模拟可比的精度,计算时间显著减少.
- 通过转移学习将模型通用到不同的治疗场所.
主要方法:
- 一个SwinUNetr模型接受了259个前列腺立体体射线疗法 (SBRT) 计划的培训.
- 预测质子点图 (PPSM) 是从患者的CT扫描和质子点图 (PSM) 中生成的.
- 转移学习适用于84个中枢神经系统 (CNS) 计划的概括.
主要成果:
- 该模型在Nvidia-A100 GPU上实现了0.07秒的剂量计算时间,比MC快100倍多.
- 对于前列腺计划,该模型的平均绝对误差 (MAE) 为DW的0.26±0.17 Gy和92.2%的马通过率.
- 在中枢神经系统计划的转移学习后,DW的MAE为0.49 ± 0.24 Gy,马通过率为90.1%.
结论:
- SwinUNetr模型为质子疗法剂量分配计算提供了高效和准确的解决方案.
- 这种深度学习方法有可能加速治疗计划,同时保持高准确度.
- 该模型在治疗场所普遍化的能力提高了其临床适用性.
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