深度学习图像重建算法对使用标准和低辐射剂量的腹部计算机断层扫描放射学特征的强度的影响
Shuo Yang1, Yifan Bie1, Lei Zhao1
1Department of Radiology, the Second Hospital of Shandong University, Jinan, China.
Quantitative imaging in medicine and surgery
|September 2, 2025
概括
深度学习图像重建 (DLIR) 提高了放射性特征 (RF) 的一致性和稳定性,特别是在中高水平,即使是低剂量CT扫描. 这表明DLIR是可靠的放射学分析传统方法的可行替代方案.
科学领域:
- 放射学
- 医学成像
- 人工智能
背景情况:
- 深度学习图像重建 (DLIR) 可以提高图像质量和降低CT扫描中的辐射剂量.
- 与传统方法相比,DLIR对放射性特征 (RF) 的强度的影响尚未得到充分证实.
研究的目的:
- 将DLIR和自适应统计代重建-Veo (ASIR-V) 对射频强度的影响进行比较.
- 在腹部CT中评估不同重建算法和剂量水平的射频一致性.
主要方法:
- 对54名肝脏质量患者进行反差增强腹部CT的回顾性分析.
- 使用ASIR-V (30%,70%) 和DLIR (低,中,高) 算法重建标准和低剂量CT数据.
- 使用PyRadiomics,使用CV,QCD和ICC指标,从18个ROI中提取837个RF并进行一致性分析.
主要成果:
- 与ASIR-V相比,在中等 (M) 和高 (H) 水平的DLIR显示出更好的射频一致性和稳定性,特别是在低剂量条件下.
- 在低剂量条件下,DLIR-M和DLIR-H之间的平均ICC值超过标准剂量下ASIR-V30%和ASIR-V70% (0. 672).
- 在低剂量条件下,在10.3%的特征中观察到强大的RF,在DLIR-M和DLIR-H之间,有23. 1%的特征显示强度.
结论:
- 大多数放射学特征在不同的重建算法和剂量水平上缺乏可重复性.
- 特别是在中高设置时,DLIR显著提高了辐射特征的一致性和稳定性,即使辐射剂量较低.
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