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通过学习单能成像能力 (Learned Monoenergetic Imaging Capability) 在能量集成探测器CT中改善狭窄性评估

Shaojie Chang¹, Emily K Koons¹, Hao Gong¹

¹Department of Radiology, Mayo Clinic, Rochester, MN, USA 55905.

Proceedings of SPIE--the International Society for Optical Engineering

|April 15, 2024

概括

一种新的AI方法LEMONADE增强了传统的CT扫描,以减少花的文物,并改善冠状动脉疾病的评估. 这项创新使得广泛使用的CT扫描仪可以使用先进的多能成像,从而改善了狭窄症的评估.

科学领域:

医疗成像医学成像
人工智能的人工智能
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 (CAD) 至关重要.
传统的CT扫描中,特别是化和支架时,花开的文物可以高估狭窄症.
来自光子计数CT的虚拟单能图像 (VMIs) 是有希望的,但在能量集成探测器 (EID) CT上并不是标准.

研究的目的:

开发和评估一个称为LEMONADE的卷积神经网络 (CNN).
提高标准EID-CT扫描仪的多能成像能力.
通过减少开花的工件来改善cCTA中狭窄的评估.

主要方法:

一个CNN (LEMONADE) 被训练在来自光子计数探测器CT的ccta数据上,使用70keVVMIs作为输入和100keVVMIs作为目标.
经过训练的LEMONADE模型应用于EID-CT数据 (幻象和患者病例).
使用LEMONADE增强图像进行了狭窄性评估,并与传统单能CT (SECT) 进行了比较.

主要成果:

在幻影研究中,LEMONADE准确量化了狭窄,显示了显著的面积减少.
在患者病例中,LEMONADE与标准SECT相比,平均直径的光缩减了12.9%.
该方法在减轻开花的工件和改善光线可见性方面表现出有效性.

结论:

在传统的EID-CT扫描仪上,LEMONADE可实现多能CT成像功能.
这种人工智能驱动的方法通过减少开花的文物,显著改善了cCTA中狭窄的评估.
由于EID-CT系统的流行,LEMONADE具有广泛临床影响的高潜力.

关键词:

心脏CT,心脏CT.能源整合探测器检测器光子计数探测器探测器的光子计数探测器狭窄的评估狭窄的评估.

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