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使用DCE-MRI中的基于像素分类的细分网络在类风湿性关节炎中完全自动量化手部突炎.

Wanxuan Fang¹, Yijun Mao¹, Haolin Wang¹

¹Graduate School of Health Sciences, Hokkaido University, North-12 West-5, Kita-Ku, Sapporo, 060-0812, Japan.

Japanese journal of radiology

|May 24, 2024

概括

这项研究引入了一种深度学习方法,用于分析动态对比度增强的MRI时间强度曲线,以自动量化类风湿性关节炎患者的突,在细分增强的Pannus中显示出高准确性.

关键词:

深度学习是一种深度学习.动态对比增强磁共振成像技术动态对比增强磁共振成像技术定量分析是一种量化分析.类风湿性关节炎类风湿性关节炎类风湿性关节炎综合性炎症综合性炎症

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科学领域:

医疗成像医学成像
人工智能的人工智能
类风湿病学类风湿病学

背景情况:

风湿性关节炎 (RA) 的诊断和监测通常涉及评估突炎,突膜的炎症.
动态对比增强磁共振成像 (DCE-MRI) 提供有关组织 perfusion 和血管性的功能信息,有助于检测活跃的炎症.
准确量化协同膜对于评估RA患者的疾病活性和治疗反应至关重要.

研究的目的:

开发和验证一种基于深度学习 (DL) 的分类方法,用于对使用DCE-MRI的RA患者手中的增强突进行细分和定量.
从DCE-MRI数据中利用时间强度曲线 (TIC) 分析来区分各种组织类型并识别炎症.

主要方法:

对28名RA患者的手部DCE-MRI扫描进行了回顾性分析.
开发了一种包含扩张因果卷积和SELU激活的DL模型,用于增强泛图细分,在像素数据和相应的TIC上进行训练.
预处理包括降噪,运动校正,对比度增强和TIC正常化. 在模型测试中使用了leave-one-out交叉验证.

主要成果:

DL模型在增强的Pannus细分中实现了高性能,像素级别灵敏度为85%,特异性为98%,准确度为99%,精度为84%,平均子得分为0.73.
用DL测量增强的Pannus体积在基于像素 (r=0.87) 和基于患者 (r=0.84) 的水平 (p <0.001对两者都) 显示出与基准真相的强烈相关性.
布兰德-阿尔特曼分析表明DL测量与地面真相之间有很好的一致性,在体积估计中平均差异很小.

结论:

拟议的基于DL的DCE-MRI TIC分析方法显示了在RA患者中自动准确细分和定量增强的突细胞的显著潜力.
这种方法可以帮助客观地评估和监测临床实践中的疾病.