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Yarong Yu¹, Dijia Wu², Ziting Lan¹

¹Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

European radiology

|December 20, 2024

概括

开发了深度学习模型,以消除晚期增强 (LIE) 图像. 剩余密集网络 (RDN) 模型显著提高了图像质量,并使精确的细胞外体积 (ECV) 量化成为可能.

科学领域:

心血管成像 - 心血管成像
医疗成像中的人工智能
放射学放射学是一门学科.

背景情况:

晚期增强 (LIE) 成像对于评估心肌痕和心血管CT中的细胞外体积 (ECV) 是至关重要的.
传统的LIE图像可能会受到噪音的影响,这可能会影响诊断准确性和量化.
深度学习 (DL) 提供了一种有前途的方法来提高图像质量和改进定量分析.

研究的目的:

开发和验证DL模型,用于否定LIE图像.
评估无色化LIE图像对ECV量化准确性的影响.
为了比较不同DL模型 (RDN和cGAN) 在图像消噪和定量分析中的性能.

主要方法:

从423名接受心脏CT的患者的LIE图像进行了回顾性分析.
开发和验证两个DL模型:剩余密集网络 (RDN) 和条件生成对抗网络 (cGAN).
图像质量指标 (SNR,CNR) 和诊断性能 (AUC) 的比较,用于在原始图像和无色图像 (LIEsingle,LIEaveraging,LIE_RDN,LIE_GAN) 之间量化LIE和ECV.

主要成果:

与原始LIE图像相比,RDN和cGAN模型都显著提高了图像质量 (SNR,CNR).
该RDN模型表现出卓越的性能,产生更高的SNR和CNR值.
与其他方法相比,LIE_RDN图像显著改善了LIE评估曲线下的面积 (AUC),并使精确的ECV量化成为可能.

结论:

RDN 深度学习模型有效地否定LIE图像,显著增强SNR和CNR.
使用RDN模型的Denoised LIE图像可以提高视觉分析的识别性.
精确的CT衍生ECV量化可以通过使用单LIE图像实现,由RDN模型表示.

关键词:

计算机断层扫描 (CT) 是一种计算机断层扫描.深度学习是一种深度学习.细胞外体积的细胞外体积.晚期增强剂是为了增加.心肌间歇性纤维化是心肌间歇性纤维化.

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