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通过整合放射学特征和神经网络,在计算机断层扫描中快速估计患者特定器官剂量

Wencheng Shao¹, Xin Lin¹, Ying Huang²

¹Institute of Radiation Medicine, Fudan University, Shanghai, China.

Quantitative imaging in medicine and surgery

|October 21, 2024

概括

这项研究开发了一个快速神经网络 (NN) 模型,使用放射学特征来预测来自CT扫描的患者特定器官辐射剂量,从而最大限度地降低癌症风险. 该模型准确地估计了剂量,从而实现了更安全的医学成像实践.

科学领域:

医学成像物理医学成像物理
放射性剂量测量放射性剂量测量放射性剂量测量
人工智能在医学中的应用

背景情况:

计算机断层扫描 (CT) 提供了详细的解剖成像,但涉及X射线辐射暴露,造成恶性瘤的风险.
准确估计患者特定器官剂量对于CT检查中的辐射风险评估至关重要.

研究的目的:

从CT扫描中开发和验证一种用于快速预测患者特定器官剂量的新方法.
利用放射学特征和神经网络 (NN) 进行准确有效的剂量预测.

主要方法:

CT DICOM图像使用自细分软件进行处理,以确定器官的感兴趣区域 (ROI).
从CT数据和ROI中提取了放射性特征,并使用蒙特卡洛 (MC) 模拟计算了参考器官剂量.
一个NN模型使用放射学特征和参考剂量进行训练,以预测患者特定器官剂量.

主要成果:

该NN模型实现了高预测准确度,最大差异低于1mGy,R平方值高达0.93.3.
在不同器官组 (头部,胸部,腹部) 中,平均绝对百分比误差 (MAPE) 从1.68%到15.2%不等.
该模型通过在培训和测试期间随机样本重新排列来证明其稳定性.

结论:

基于放射学的NN模型从CT扫描中准确预测患者特定的器官剂量.
预测过程非常高效,在单个CPU上用不到一秒的时间完成.
这种方法为临床应用提供了一个用户友好的工具,提高了医学成像中的辐射安全性.

关键词:

计算机断层扫描 (CT) 扫描神经网络 (NN) 是一个神经网络.器官剂量器官剂量器官剂量患者特定的患者特定的患者.放射学特征放射学特征放射学特征

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