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深度学习方法用于检测心脏磁共振中的痕存在,添加衍生参数图像

Francesca Righetti¹, Giulia Rubiu¹, Marco Penso^2,3

¹Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, P.zza L. da Vinci 32, 20133, Milan, Italy.

Medical & biological engineering & computing

|August 6, 2024

概括

这项研究引入了一个卷积神经网络 (CNN),用于在没有对比剂的心脏磁共振 (CMR) 图像中检测心肌痕组织. 美国有线电视新闻网 (CNN) 在对患者进行分类方面表现出很高的准确性,为患者提供了潜在的查工具.

科学领域:

心脏病学心脏病学
医疗成像医学成像
人工智能的人工智能

背景情况:

心肌痕组织评估对于心血管疾病管理至关重要.
晚期加多增强 (LGE) 心脏磁共振 (CMR) 是黄金标准,但需要对比剂.
需要使用非对比的方法来检测痕.

研究的目的:

开发和评估一个卷积神经网络 (CNN) 用于检测心肌痕组织使用无对比的Cine CMR图像.
将CNN的表现与LGE-CMR图像的专家解释进行比较.
评估CNN作为LGE-CMR处方查工具的潜力.

主要方法:

开发了一个CNN来分析从电影CMR图像中获得的参数图像.
参数图像使用里埃和单基信号分析来计算,以提取左心室动态特征.
美国有线电视新闻网 (CNN) 在206名患者 (158名痕,48名控制) 的数据上进行了培训和测试,LGE-CMR作为基底真相.

主要成果:

对于个人切片分类,CNN在ROC曲线下的面积达到0.86.6.
美国有线电视新闻网 (CNN) 在患者级别分类 (控制与病理) 中表现出了1.0的预测准确性.
切片级性能包括精度0.79,F10.81,灵敏度0.9,特异性0.65,NPV0.83和PPV0.77.

结论:

拟议的CNN方法可以在没有对比度的CMR图像中进行痕检测.
患者级分类的准确性表明,它有可能作为一种初步查工具,用于指导LGE-CMR决策.
这种方法可以帮助优化LGE-CMR处方,特别是在不确定的情况下.

关键词:

心脏磁共振成像心脏磁共振成像卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.磁共振图像的分类磁共振图像的分类参数图像是指参数图像中的参数图像.

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