基于深度学习的重建算法的影响CT视觉特征的动脉斑块的放射性特征
Hanzhe Wang1, Jingkai Xu1, Chengeng Ye1
1Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Journal of applied clinical medical physics
|November 14, 2025
概括
深度学习重建 (DLIR) 和自适应的统计代重建-Veo (ASIR-V) 显著改变状板中的CT放射性特征,其中纹理特征显示最稳定. 对于可靠的放射性生物标志物,建议优先考虑坚固的纹理特征,并使用较低强度的DLIR.
科学领域:
- 医疗成像医学成像
- 放射学 放射学是一门学科.
- 心血管成像 - 心血管成像
背景情况:
- 动脉斑块的放射学分析有助于评估心血管风险.
- 像DLIR和ASIR-V这样的先进CT重建算法的对放射性特征可重现性的影响尚不清楚.
研究的目的:
- 评估DLIR和ASIR-V如何影响心血管斑块中的CT放射性特征.
- 在不同的重建方法中比较放射性特征的稳定性.
主要方法:
- 通过CT血管学,对来自76名患者的104个动脉斑进行了回顾性分析.
- 使用过后投影 (FBP),ASIR-V (30-80%) 和DLIR (DL,DM,DH) 的图像重建.
- 从2D和3D图像中提取和比较214个放射性特征 (第一阶段,纹理),用类内相关系数 (ICC) 评估可靠性.
主要成果:
- 重建算法显著影响了放射性特征,在更高的ASIR-V和DLIR强度下,不受影响的特征较少.
- 纹理特征在重建协议和域 (原始,波形) 中表现出最高的稳定性.
- 2D病变显示出比3D病变更多不受影响的特征,而3D病变的第一阶段特征具有很好的一致性.
结论:
- 无论是ASIR-V还是DLIR,都会对带斑块的放射力产生重大影响,强度增加会导致更大的特征不稳定性.
- 在各种重建方法中,纹理特征是最坚固的.
- 以稳定性为导向的方法,强调纹理特征和较低强度的DLIR,对于开发可概括的放射性生物标志物至关重要.
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