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超分辨率的深度学习重建在冠状动脉计算机断层扫描血管造影,以评估冠状动脉和支架光:一个初始的经验

Makoto Orii¹, Misato Sone², Takeshi Osaki²

¹Department of Radiology, Iwate Medical University, 2-1-1, Idaidori, Yahaba, 028-3695, Japan. kori931@gmail.com.

BMC medical imaging

|October 31, 2023

概括

超分辨率深度学习重建 (SR-DLR) 显著提高了冠状动脉计算机断层扫描血管学 (CCTA) 图像质量. 与基于模型的代重建相比,SR-DLR降低了噪音并提高了信号与噪音的比率,从而使冠状动脉和支架灯光的可视化更清晰.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
人工智能在医学中的应用

背景情况:

冠状动脉计算机断层扫描血管造影 (CCTA) 对于诊断冠状动脉疾病至关重要.
超高空间分辨率计算机断层扫描 (UHRCT) 和深度学习重建 (SR-DLR) 显示了增强CCTA图像质量的潜力.
在CCTA中SR-DLR的特殊好处,特别是与传统方法相比,需要进一步研究.

研究的目的:

在CCTA中,将SR-DLR和基于模型的代重建 (MBIR) 之间的冠状动脉和支架亮度的图像质量进行比较.
为了定量评估图像噪声,信号与噪声比率 (SNR),对比与噪声比率 (CNR),支架支架厚度和支架灯光直径.

主要方法:

预计将招募70名通过320检测器行CT扫描仪接受CCTA的患者.
图像分析包括对大动脉,左心室和心室的噪声测量,对近邻冠状动脉计算了SNR和CNR.
对20名带有支架的患者进行了支架支架厚度和光径的定量评估.

主要成果:

与MBIR相比,SR-DLR的图像噪声显著降低 (中位数为22.1 HU,而不是27.4 HU).
与MBIR相比,SR-DLR的SNR (中位数为16.3对13.7) 和CNR (中位数为24.4对19.2) 显著高于MBIR.
对于SR-DLR,体支架显著较薄 (中位数为0.68毫米而不是0.81毫米),并且体内光量显著较大 (中位数为1.84毫米而不是1.52毫米).

结论:

通过减少噪音和增强SNR和CNR,SR-DLR显著提高了CCTA图像质量.
通过SR-DLR,可以更准确地评估冠状动脉和支架内流通量,观察到更薄的支架和更大的流通量直径.
在常规实践中,SR-DLR有望提高CCTA的临床实用性,尽管需要进一步的验证研究.

关键词:

对比度与噪声比的比率.冠状动脉计算机断层扫描血管图谱.基于模型的代重建.信号与噪声的比率.超级分辨率深度学习重建

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