在双能计算机断层扫描中优化材料组成的确定:对线性模型和完全连接的神经网络进行比较研究
Alexandr Malusek1,2, Sofie Malmodin3, Maria Magnusson2,3
1Department of Health, Medicine and Caring Sciences, Linköping University, SE-581 83 Linköping, Sweden.
Radiation protection dosimetry
|March 13, 2026
概括
线性回归和神经网络可以准确地预测用于放射治疗的双能量CT (DECT) 中的组织元素组成. 神经网络确保了物理可信的结果,这对于人工智能驱动的治疗规划至关重要.
科学领域:
- 医疗成像医学成像
- 计算物理 计算物理
- 辐射瘤学 辐射瘤学
背景情况:
- 在双能计算断层扫描 (DECT) 中精确的元素分解对于精确的辐射治疗规划至关重要.
- 目前的方法需要强大的算法来预测组织元素组成.
研究的目的:
- 为了比较线性回归和完全连接的神经网络 (FCNNs) 在DECT中对voxel-wise元素组成的预测.
- 在不同的噪声条件下使用具有现实可变性的合成数据评估模型性能.
主要方法:
- 开发和比较线性回归和FCNN模型的元素分解.
- 使用合成DECT数据集模拟患者内和患者间的变化和噪声.
主要成果:
- 这两种模型在无噪声条件下表现良好;线性回归显示出略低的误差.
- 在噪音条件下,这两种模型的性能都下降了,但线性回归保持了较低的数值误差.
- FCNN 一贯生成物理可信的 (非负的) 元素质量分数估计.
结论:
- 线性回归和FCNN对于DECT中的元素分解是有效的.
- 通过确保物理可信的结果,FCNNs提供了一个优势,这对于临床应用至关重要.
- 这些人工智能驱动的模型可以在集成到代重建算法中时增强辐射治疗计划.
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