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对低对比度介质的图像质量评估,使用深度学习图像重建算法在潜在的ECG触发冠状动脉CT血管学中进行评估

Dian Yuan¹, Luotong Wang², Peijie Lyu¹

¹The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, Henan Province, 450052, China.

The international journal of cardiovascular imaging

|May 9, 2024

概括

深度学习图像重建使冠状动脉CT血管学图像质量优异,对比介质剂量显著降低. 这项研究表明,0.5mL/kg的对比剂量提供了足够的增强和诊断信心.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
心血管成像 - 心血管成像

背景情况:

冠状动脉CT血管造影 (CCTA) 对于诊断冠状动脉疾病至关重要.
减少对比介质 (CM) 的体积是理想的,以尽量减少患者的风险和医疗保健成本.
深度学习图像重建 (DLIR) 算法显示了在较低CM剂量下改善图像质量的潜力.

研究的目的:

评估低剂量对比剂介质注射协议与DLIR相结合对CCTA图像质量的影响.
为了确定诊断质量的最小有效CM体积,CCTA使用DLIR.IR来确定.

主要方法:

一项前性研究,涉及210名患者,随机分配到三个CM体积组 (0.7,0.6和0.5毫升/公斤).
所有的CCTA都使用前性ECG触发协议与高级DLIR (DLIR-H) 算法进行.
两个放射科医生评估了客观CT值和主观图像质量/诊断信心评分.

主要成果:

在三组中,客观图像质量 (大动脉和冠状动脉CT值) 或主观得分没有显著差异 (p > 0.05).
在CM剂量低至0.5mL/kg时,获得了优异的CCTA图像质量和诊断信心.
随着DLIR重建,即使在最低的CM剂量下,也始终观察到足够的冠状动脉增强.

结论:

使用DLIR重建的CCTA可以使用0.5mL/kg的低CM剂量实现出色的图像质量和诊断信心.
DLIR有助于显著减少CM体积,而不会影响CCTA的诊断性能.
通过更低的管电压设置,可以进一步降低CM剂量.

关键词:

计算机断层扫描血管图谱.对比媒介对比媒介.冠状动脉血管冠状动脉血管深度学习图像重建图像重建

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