使用基于卷积神经网络的单能CT数据进行伪双能CT衍生映射
Yuki Yuasa1, Takehiro Shiinoki1, Koya Fujimoto1
1Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Yamaguchi, Japan.
BJR open
|November 9, 2023
概括
一个新的卷积神经网络模型从低能量的CT扫描生成伪双能CT图像. 这允许从单能CT数据创建虚拟的非对比和图.
科学领域:
- 医疗成像医学成像
- 放射学中的人工智能
- 计算成像技术的成像
背景情况:
- 双能CT (DECT) 提供了有价值的材料分解信息,但需要专门的扫描仪.
- 从单能CT产生DECT衍生图像可以扩大对高级成像分析的访问.
- 低能量的CT图像 (CT_low) 是广泛可用的,可以作为高级重建的基础.
研究的目的:
- 开发一个卷积神经网络 (CNN) 模型,从CT_low图像中生成伪高能CT (CT_pseudo_high) 图像.
- 为胸部和腹部区域创建伪图 (IM_pseudo) 和伪虚拟非对比 (VNC_pseudo) 图像.
- 评估生成的伪DECT图像和特定材料图像的准确性和性能.
主要方法:
- 包括80名接受DECT检查的患者,数据分为培训 (55),验证 (5) 和测试 (20).
- 采用ResUnet模型进行图像生成,使用CT_low和高能CT (CT_high) 图像进行训练.
- 用CT值,图像噪声,平均绝对误差 (MAE) 和直方图交点 (HIs) 来评估模型性能.
主要成果:
- 与CT_pseudo_high图像相比,CT_pseudo_high图像的CT值显示的平均差异小于6HU.
- 在CT_pseudo_high图像中的图像噪声明显低于CT_high图像.
- 平均MAE低于15HU,组图交叉点接近1000,表明高度相似.
结论:
- 开发的CNN模型成功地从单能CT数据中生成伪DECT图像和特定材料图像.
- 这种方法可以创建先进的成像产品,如图和VNC图像,只使用低能量的CT输入.
- 这些发现表明,DECT衍生分析在不需要双重能量获取的情况下,有可能得到更广泛的应用.
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