基于深度学习的不确定性预测可变形图像登记的轮传播和剂量积累在线自适应性放射治疗
A Smolders1,2, A Lomax1,2, D C Weber1,3,4
1Paul Scherrer Institute, Center for Proton Therapy, Switzerland.
Physics in medicine and biology
|October 11, 2023
概括
本研究引入了深度学习方法,以预测可变形图像注册 (DIR) 的不确定性,这是适应性放射治疗的关键步骤. 这些方法可以实现更可靠的轮传播和剂量积累,提高治疗精度.
科学领域:
- 医学物理 医学物理
- 放射疗法是一种放射治疗.
- 图像分析 图像分析
背景情况:
- 在线适应性放射疗法 (ART) 需要自动化来进行轮传播和剂量积累.
- 可变形图像注册 (DIR) 对ART至关重要,但由于算法不一致而面临不信任.
- 预测DIR不确定性对于临床采用至关重要.
研究的目的:
- 开发深度学习方法来预测DIR的不确定性.
- 将这种不确定性传播到临床相关的轮和剂量指标中.
- 为了使DIR在适应性放射治疗工作流程中的临床使用成为可能.
主要方法:
- 训练有素的监督和无监督神经网络来预测可变形向量场 (DVF) 不确定性.
- 将两种模型的预测合并为一种综合方法.
- 在轮和累积剂量不确定性中传播的DVF不确定性.
主要成果:
- 无监督和组合模型准确地预测了DIRLAB数据集上的不确定性.
- 在肺癌患者的轮不确定性方面实现了预期校准误差<3%.
- 概率剂量体积图包括来自多个DIR算法的累积剂量.
- 无监督模型证明了在不同的DIR算法中无需重新培训的可用性.
结论:
- 开发了用于DIR不确定性预测的新型深度学习方法.
- 方法快速,提供高质量的估计,并且算法不可知.
- 允许将DIR不确定性集成到临床工作流程中,而无需更改DIR实现.
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