一个用于甲状腺细分和剂量评估的多尺度网络,用于高甲状腺患者的不确定性量化
1Department of Nuclear Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Medical physics
|October 22, 2025
概括
这项研究开发了一种深度学习模型,用于使用医学内部辐射剂量 (MIRD) 方案准确计算甲状腺吸收剂量,用于治疗甲状腺功能障碍. 该模型提高了细分的准确性,并量化了不确定性,改善了个性化剂量计.
科学领域:
- 医学物理 医学物理
- 辐射科学 辐射科学
- 人工智能在医学中的应用
背景情况:
- 精确的吸收剂量评估对于使用放射性 (131I) 进行个性化甲状腺功能障碍治疗至关重要.
- 使用医学内部辐射剂量 (MIRD) 方案计算甲状腺剂量存在挑战,特别是在图像细分和不确定性量化方面.
- 从MIRD和马里内利-奎姆比公式对剂量估计进行比较需要进一步调查.
研究的目的:
- 设计一个基于MIRD模式的多尺度深度学习模型,以提高对高甲状腺患者的吸收剂量计算的准确性和可靠性.
- 为了改进剂量计,将不确定性量化纳入细分过程.
- 为了比较MIRD方案和马里内利-奎姆比公式之间的吸收剂量估计值.
主要方法:
- 开发了一个UNet++架构,包含多个尺度的剩余和多头注意模块 (MSRA-UNet++).
- 实施了基于信息的不确定性量化模块,用于细分信心评估.
- 创建了针对患者的甲状腺voxel幻影,使用蒙特卡洛方法计算S值,并使用MIRD图表评估吸收剂量.
主要成果:
- MSRA-UNet++模型在多器官细分方面实现了高性能,子相似系数 (DSC) 为88.23%和豪斯多夫距离95% (HD95) 为9.43像素,用于甲状腺细分.
- 雅卡德指数 (JI) 在临床数据集上与UNet++相比提高了6.17%,在甲状腺边缘量化不确定性.
- 马里内利-奎姆比公式高估了MIRD方案的吸收剂量,平均为7.74%.
结论:
- 该MSRA-UNet++网络提供精确的甲状腺细分与估计的不确定性,通过组合的细分不确定性和蒙特卡洛模拟提高S值的可靠性.
- 这项研究是首次将不确定性量化纳入甲状腺功能过高患者的CT图像细分,利用患者特定的幻影和放射性动力学进行基于MIRD的剂量评估.
- 代码将在GitHub上发布,以促进进一步的研究和应用.
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