英国生物库研究中使用自动批量处理对心脏磁共振成像分割、特征跟踪、主动脉血流和原生 T1 分析进行质量控制。

European heart journal. Imaging methods and practice Pub Date : 2024-09-16 eCollection Date: 2024-07-01 DOI:10.1093/ehjimp/qyae094
Sucharitha Chadalavada, Elisa Rauseo, Ahmed Salih, Hafiz Naderi, Mohammed Khanji, Jose D Vargas, Aaron M Lee, Alborz Amir-Kalili, Lisette Lockhart, Ben Graham, Mihaela Chirvasa, Kenneth Fung, Jose Paiva, Mihir M Sanghvi, Gregory G Slabaugh, Magnus T Jensen, Nay Aung, Steffen E Petersen
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引用次数: 0

摘要

目的:自动算法经常用于分析心脏磁共振(CMR)图像。验证这种方法的数据输出可靠性对于实现广泛应用至关重要。我们概述了使用自动批处理进行图像分析的视觉质量控制 (VQC) 流程。我们评估了英国生物库 CMR 扫描中自动分析的性能以及用统计离群值(SO)去除方法取代视觉检查的可靠性:我们纳入了英国生物库 COVID-19 成像研究中的 1987 份 CMR 扫描。我们使用批处理软件(Circle Cardiovascular Imaging Inc.-CVI42)自动提取心腔容积数据、应变、原生 T1 和主动脉血流数据。六名经验丰富的临床医师使用标准化方法和定制的 R Shiny 应用程序对自动分析输出结果(62 000 个视频和 2000 张图像)进行了目测检查。对观察者之间的差异性进行了评估。在一组健康人(n = 1069)中,将通过 VQC 的扫描数据与 SO 移除 QC 方法进行了比较。自动分割得到了高度评价,超过 95% 的扫描通过了 VQC。观察者之间的整体一致性非常好(Gwet's AC2 0.91;95% 置信区间 0.84,0.94)。通过 VQC 或去除 SO 得出的健康人总体数据没有差异:结论:使用 CVI42 原型对英国生物库 CMR 扫描进行的自动图像分析显示出很高的质量。使用这些自动算法分析的较大型英国生物库数据集不需要深入的 VQC。作为质量控制措施,去除 SO 即可,操作员可根据人群或研究目标酌情进行视觉检查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quality control of cardiac magnetic resonance imaging segmentation, feature tracking, aortic flow, and native T1 analysis using automated batch processing in the UK Biobank study.

Aims: Automated algorithms are regularly used to analyse cardiac magnetic resonance (CMR) images. Validating data output reliability from this method is crucial for enabling widespread adoption. We outline a visual quality control (VQC) process for image analysis using automated batch processing. We assess the performance of automated analysis and the reliability of replacing visual checks with statistical outlier (SO) removal approach in UK Biobank CMR scans.

Methods and results: We included 1987 CMR scans from the UK Biobank COVID-19 imaging study. We used batch processing software (Circle Cardiovascular Imaging Inc.-CVI42) to automatically extract chamber volumetric data, strain, native T1, and aortic flow data. The automated analysis outputs (∼62 000 videos and 2000 images) were visually checked by six experienced clinicians using a standardized approach and a custom-built R Shiny app. Inter-observer variability was assessed. Data from scans passing VQC were compared with a SO removal QC method in a subset of healthy individuals (n = 1069). Automated segmentation was highly rated, with over 95% of scans passing VQC. Overall inter-observer agreement was very good (Gwet's AC2 0.91; 95% confidence interval 0.84, 0.94). No difference in overall data derived from VQC or SO removal in healthy individuals was observed.

Conclusion: Automated image analysis using CVI42 prototypes for UK Biobank CMR scans demonstrated high quality. Larger UK Biobank data sets analysed using these automated algorithms do not require in-depth VQC. SO removal is sufficient as a QC measure, with operator discretion for visual checks based on population or research objectives.

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