对贝叶斯深度网络不确定性的研究及其应用于合成CT生成,用于MR-only放射治疗治疗规划
Max Wai-Kong Law1, Mei-Yan Tse1, Leon Chin-Chak Ho1
1Medical Physics Department, Hong Kong Sanatorium and Hospital, Hong Kong SAR, China.
Medical physics
|September 4, 2023
概括
这项研究引入了一个新的贝叶斯深度网络,用于在MR-only放射治疗中生成合成CT图像. 该模型准确地估计了合成CT的不确定性,优于现有的方法,并且在没有原始CT数据的情况下预测错误.
科学领域:
- 医疗成像医学成像
- 放射治疗 物理 物理
- 人工智能在医学中的应用
背景情况:
- 合成CT (sCT) 对于仅MR的放射治疗计划至关重要,因为它缺乏电离辐射,与CT相比,软组织对比度优越.
- 在没有原始CT数据的MR-only工作流程中评估sCT质量仍然是一个挑战.
研究的目的:
- 开发一种新的贝叶斯深度网络,用于生成sCT图像和估计sCT不确定性,仅用于MR放射治疗.
- 建立贝叶斯网络不确定性和sCT不确定性之间的可量化的关系.
主要方法:
- 一个混合贝叶斯深度网络与概率权重被制定来产生sCT和估计不确定性.
- 数学表达式是为了量化贝叶斯式和sCT不确定性而衍生出来的,它们与平均绝对误差 (MAE) 相联系.
- 该模型在59名前列腺癌患者身上进行了评估,将其准确性和剂量测量性能与基于Atlas和基于GAN的方法进行比较.
主要成果:
- 提出的贝叶斯模型实现了44.33 HU的MAE,超过了竞争方法 (UcGAN: 52.51,MRCAT: 54.87).
- 用马射率 (98.68%) 测量的剂量测量精度与现有方法相比较.
- 贝叶斯不确定性与sCT不确定性有很强的相关性 (R2=0.98),在没有原始CT数据的情况下有效预测MAE.
结论:
- 一个新的贝叶斯深度网络有效地生成合成CT图像,并估计它们的不确定性仅用于MR放射治疗.
- 该模型展示了与当前方法相比较的CT数量和剂量准确性.
- 建议的贝叶斯不确定性是MR-only工作流程中合成CT误差的可靠指标.
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