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在双能光子计数CT中对HU到RED校准的有效原子数进行深度学习估计

Kihong Son¹, Daehong Kim²

¹Medical Information Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea.

Scientific reports

|December 23, 2025

概括

一种新的深度学习模型准确地预测有效原子数 (EAN) 并改善双能CT (DECT) 中的相对电子密度 (RED) 估计. 这种进步增强了材料的分化,并支持精确的剂量计算.

科学领域:

医学物理医学物理
医疗成像中的人工智能

背景情况:

精确的材料表征在双能CT (DECT) 中至关重要,用于诸如放射治疗规划等应用.
估计材料属性的常规方法,如有效原子数 (EAN) 和相对电子密度 (RED),都有局限性.

研究的目的:

开发和评估一个深度学习 (DL) 框架,用于精确的 EAN 预测,以改善 DECT.中 RED 估计.
为了比较DL方法的性能与传统的拉瑟福和静电测量方法.

主要方法:

一个修改后的U-Net深度学习模型被训练在来自光子计数探测器CT的合成光谱数据上.
该模型直接估计了EAN,然后使用基于物理的模型将其转换为RED.
对八种材料的性能进行了评估,使用平均绝对误差 (MAE) 和余量,将DL与传统方法进行比较.

主要成果:

与拉瑟福 (1.59%) 和静态测量 (1.54%) 方法相比,DL模型实现了EAN (0.08%) 的显著较低的MAE.
基于DL的RED估计得出MAE为0.62%,残留量最小.
与传统方法 (R2 ≈ 0.9995) 相比,DL方法在 ΔHU-RED 校准曲线中显示出略高的线性 (R2 = 0.9998).

结论:

深度学习框架显著提高了DECT幻影研究中EAN和RED估计的准确性.
通过DL方法实现的增强HU-RED线性提高了材料差异化能力.
这种DL方法有可能在未来的临床DECT应用中进行更精确的剂量计算.

关键词:

深度学习是一种深度学习.双能量CT是双能量CT.有效原子号是有效的原子号.在HU-RED的校准中,相对电子密度的相对电子密度.

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