基于磁共振成像的质子剂量计算用于使用深度学习的骨盆瘤.
Liheng Tian1, Laura Tsu2, Paulin Vehling2
1Department of Physics, TU Dortmund University, Otto-Hahn-str 4, Dortmund, Dortmund, NRW, 44227, GERMANY.
Physics in medicine and biology
|January 16, 2026
概括
深度学习模型使MRI只能计算盆腔中的质子疗法剂量. 直接预测管道显示对MRI扭曲的稳定性,而两步管道提供较低剂量预测错误.
科学领域:
- 医学物理 医学物理
- 辐射疗法 辐射疗法
- 人工智能在医学中的应用
背景情况:
- 只有磁共振成像 (MRI) 的质子疗法提供了卓越的软组织对比度和精确的剂量递送.
- 传统的剂量计算受到MRI扫描中缺乏电子密度信息的阻碍.
- 准确的剂量计算对于有效的质子疗法规划至关重要.
研究的目的:
- 调查两种基于深度学习 (DL) 的MRI-only质子剂量计算管道用于盆腔治疗的可行性.
- 为了评估这些管道对MRI强度扭曲的稳定性.
- 将基于DL的剂量预测与蒙特卡洛模拟的准确性进行比较.
主要方法:
- 开发了两个DL管道:一种两步方法 (MRI到合成CT,然后剂量预测) 和一种直接方法 (直接在MRI上进行剂量预测).
- 蒙特卡洛模拟用于生成基准真相剂量分布,用于训练和验证,使用120名骨盆患者的MRI-CT数据.
- 管道性能被评估使用马传递率和平均相对误差 (ARE) 对于个别的笔束和治疗计划,引入MRI强度扭曲.
主要成果:
- 两条管道都实现了高马通道率 (>99.2%).
- 两步管道证明了低ARE (0.11%的笔束,2.63%的治疗计划).
- 直接管道显示了更高的ARE (笔光束为0.16%,治疗计划高达6.11%),但表现出对MRI强度扭曲的稳定性.
结论:
- 基于深度学习的MRI-only质子剂量计算对于骨盆区域治疗是可行的.
- 直接DL管道显示了学习MRI-to-dose映射的希望,尽管需要进一步优化.
- 两步DL管道提供了精确的质子剂量预测,最小的错误,适合临床实施.
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