心脏磁共振成像细分,特征跟踪,大动脉流量和本地T1分析的质量控制,使用自动批量处理在英国生物银行研究中

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Sucharitha Chadalavada^1,2, Elisa Rauseo^1,2,3, Ahmed Salih^1,4

¹William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK.

European heart journal. Imaging methods and practice

|October 10, 2024

概括

自动心磁共振 (CMR) 图像分析对于像英国生物银行这样的大数据集是可靠的. 删除统计异常值足以进行质量控制,减少了对广泛视觉检查的需求.

科学领域:

心血管成像 - 心血管成像
医学图像分析医学图像分析
人工智能在医学中的应用

背景情况:

自动化算法越来越多地用于心脏磁共振 (CMR) 图像分析.
验证自动化数据输出的可靠性对于广泛的临床采用至关重要.
英国生物银行COVID-19成像研究为评估自动化分析方法提供了大量数据集.

研究的目的:

概述用于自动化CMR图像分析的视觉质量控制 (VQC) 过程.
评估英国生物银行CMR扫描中自动化分析的性能.
评估统计异常值 (SO) 删除作为VQC的替代品的可靠性.

主要方法:

包括1987年来自英国生物银行COVID-19成像研究的CMR扫描.
使用的Circle Cardiovascular Imaging Inc.-CVI42软件用于自动提取体积,应变,T1和流量数据.
六名临床医生使用标准化方法和R Shiny应用程序进行了VQC;评估了观察者之间的变化.

主要成果:

自动化细分得到了很高的评分,超过95%的扫描通过了VQC.
总体而言,VQC的观察者间一致性非常好 (Gwet的AC2 0.91).
在健康个体 (n=1069) 中,没有观察到从VQC或SO去除中获得的数据的显著差异.

结论:

使用CVI42原型用于英国生物库扫描的自动化CMR图像分析表明高质量.
用这些算法分析的大型英国生物库数据集不需要广泛的VQC.
SO去除是足够的质量控制措施,根据研究需要,操作员可以根据视觉检查的自由裁量权来进行视觉检查.

关键词:

闪闪发光的应用程序自动化图像分析系统心脏磁共振成像 - 心脏磁共振成像机器学习是机器学习.质量控制质量控制质量控制

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